Journal articles on the topic 'Modified fuzzy c-means (FCM)'
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Huang, Cheng Quan. "A Modified Fuzzy C-Mean Algorithm for Automatic Clustering Number." Applied Mechanics and Materials 333-335 (July 2013): 1418–21. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1418.
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FCM(Fuzzy C-Means) algorithm is an important algorithm in cluster analysis. It plays an significant role in theory and practice. However, the clustering number of FCM algorithm needs to be set beforehand. This paper proposes an automatic clustering number determination for the classical FCM(Fuzzy C-Means) algorithm. The proposed automatic clustering number determination is based on the cardinality of clustering fuzzy membership used in the CA(Competitive Agglomeration) algorithm. The effectiveness of the proposed algorithm, along with a comparison with CA algorithm, has been showed both qualitatively and quantitatively on a set of real-life datasets.
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Zhang, Wenyuan, Tianyu Huang, and Jun Chen. "A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm." Mathematical Problems in Engineering 2019 (June 18, 2019): 1–17. http://dx.doi.org/10.1155/2019/5984649.
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This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.
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Yang, Qing, Zhi Qiang Wang, and Yan Xu. "Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing." Applied Mechanics and Materials 624 (August 2014): 536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.624.536.
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Considering the problem that the traditional fuzzy c-means (FCM) image segmentation algorithm is often caught in a specific range in local search and fails to get the globally optimal solution, this paper proposed a modified FCM algorithm based on chaotic simulated annealing (CSA). It traverse all the states without repetition within a certain range to calculate the optimal solution. Experimental results show that our method converges more quickly and accurately to the global optimal and proves a promise global optimization method of high adaptability and feasibility.
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Dewi Paramitha, Ida Ayu Shinta, Gusti Made Arya Sasmita, and I. Made Sunia Raharja. "Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means." Majalah Ilmiah Teknologi Elektro 19, no. 1 (October 15, 2020): 95. http://dx.doi.org/10.24843/mite.2020.v19i01.p14.
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Snort is one of open source IDS to detect intrusion or potentially malicious activity on network traffic. Snort will give alert for every detected intrusion and write the alerts in log. Log data in IDS Snort will help network administrator to analyze the vulnerability of network security system. Clustering algorithm such as FCM can be used to analyze the log data of IDS Snort. Implementation of the algorithm is based on Python 3 and aims to cluster alerts in log data into 4 risk categories, such as low, medium, high, and critical. The outcome of this analysis is to show cluster results of FCM and to visualize the types of attacks that IDS Snort has successfully detected. Evaluation process is done by using Modified Partition Coefficient (MPC) to determine the validity of FCM.
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Szilágyi, László, Szidónia Lefkovits, and Sándor M. Szilágyi. "Self-Tuning Possibilistic c-Means Clustering Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, Supp01 (November 5, 2019): 143–59. http://dx.doi.org/10.1142/s0218488519400075.
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The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due to the presence of noise, PCM has no chance to recover and produce a fine partition. This paper proposes a two-stage c-means clustering algorithm to tackle with most problems enumerated above. In the first stage called initialization, FCM with two modifications is performed: (1) extra cluster added for noisy data; (2) extra variable and constraint added to handle clusters of various diameters. In the second stage, a modified PCM algorithm is carried out, which also contains the cluster width tuning mechanism based on which it adaptively updates the possibilistic penalty terms. The proposed algorithm has less parameters than PCM when the number of clusters is [Formula: see text]. Numerical evaluation involving synthetic and standard test data sets proved the advantages of the proposed clustering model.
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Liu, Xiao Li, Yu Ting Guo, Jun Kong, and Jian Zhong Wang. "A Modified Fuzzy C-Means Algorithm Brain MR Images Segmentation with Bias Field Compensation." Advanced Materials Research 756-759 (September 2013): 1349–55. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1349.
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Segmentation of brain magnetic resonance (MR) images is always required as a preprocessing stage in many brain analysis tasks. Nevertheless, the bias field (BF, also called intensity in-homogeneities) and noise in the MRI images always make the accurate segmentation difficult. In this paper, we present a modified FCM algorithm for bias field estimation and segmentation of brain MRI. Our method is formulated by modifying the objective function of the standard FCM algorithm. It aims to compensate for bias field and incorporate both the local and non-local information into the distance function to restrain the noise of the image. We have conducted extensive experimental and have compared our method with different types of FCM extension methods using simulated MRI images. The results show that our proposed method can deal with the bias field and noise effectively and outperforms other methods.
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Rustam, Koredianto Usman, Mudyawati Kamaruddin, Dina Chamidah, Nopendri, Khaerudin Saleh, Yulinda Eliskar, and Ismail Marzuki. "MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS." Acta Polytechnica 61, no. 2 (April 30, 2021): 364–77. http://dx.doi.org/10.14311/ap.2021.61.0364.
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A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM). However, the PFCM algorithm is only applicable to complete data sets. Therefore, this research modified the PFCM for clustering incomplete data sets to OCSPFCM and NPSPFCM with the performance evaluated based on three aspects, 1) accuracy percentage, 2) the number of iterations, and 3) centroid errors. The results showed that the NPSPFCM outperforms the OCSPFCM with missing values ranging from 5% − 30% for all experimental data sets. Furthermore, both algorithms provide average accuracies between 97.75%−78.98% and 98.86%−92.49%, respectively.
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Rayala, Venkat, and Satyanarayan Reddy Kalli. "Big Data Clustering Using Improvised Fuzzy C-Means Clustering." Revue d'Intelligence Artificielle 34, no. 6 (December 31, 2020): 701–8. http://dx.doi.org/10.18280/ria.340604.
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Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises the encoder decoder Convolutional Neural Network (CNN) model and Fuzzy C-means (FCM) technique to enhance the clustering mechanism. Encoder decoder based CNN is used for learning feature and faster computation. In general, FCM, we introduce a function which measure the distance between the cluster center and instance which helps in achieving the better clustering and later we introduce Optimized Encoder Decoder (OED) CNN model for improvising the performance and for faster computation. Further in order to evaluate the proposed mechanism, three distinctive data types namely Modified National Institute of Standards and Technology (MNIST), fashion MNIST and United States Postal Service (USPS) are used, also evaluation is carried out by considering the performance metric like Accuracy, Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Moreover, comparative analysis is carried out on each dataset and comparative analysis shows that IFCM outperforms the existing model.
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Wan, Renxia, Yuelin Gao, and Caixia Li. "Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets." International Journal of Data Warehousing and Mining 8, no. 4 (October 2012): 82–107. http://dx.doi.org/10.4018/jdwm.2012100104.
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Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes.
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Liu, Qing Feng. "An Extensional Clustering Algorithm of FCM Based on Intuitionistic Extension Index." Advanced Materials Research 490-495 (March 2012): 1372–76. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1372.
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The fuzzy C-means algorithm is an iterative algorithm in which the desired number of clusters C and the initial clustering seeds has to be pre-defined. The seeds are modified in each stage of the algorithm and for each object a degree of membership to each of the clusters is estimated. In this paper, an extensional clustering algorithm of FCM based on an intuitionistic extension index, denoted E-FCM algorithm, is proposed. For comparing the performance of the above mentioned two algorithms, the experimental results of three benchmark data sets show that the E-FCM algorithm outperforms the FCM algorithm.
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Benzian, Yaghmorasan, and Nacéra Benamrane. "New FCM Segmentation Approach Based on Multi-Resolution Analysis." International Journal of Fuzzy System Applications 7, no. 4 (October 2018): 100–114. http://dx.doi.org/10.4018/ijfsa.2018100105.
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This article presents a modified Fuzzy C Means segmentation approach based on multi-resolution image analysis. Fuzzy C-Means standard methods are improved through fuzzy clustering at different image resolution levels by propagating fuzzy membership values pyramidally from a lower to a higher level. Processing at a lower resolution image level provides a rough pixel classification result, thus, a pixel is assigned to a cluster to which the majority of its neighborhood pixels belongs. The aim of fuzzy clustering with multi-resolution images is to avoid pixel misclassification according to the spatial cluster of the neighbourhood of each pixel in order to have more homogeneous regions and eliminate noisy regions present in the image. This method is tested particularly on samples and medical images with gaussian noise by varying multiresolution parameter values for better analysis. The results obtained after multi-resolution clustering are giving satisfactory results by comparing this approach with standard FCM and spatial FCM ones.
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Supartha, I. Kadek Dwi Gandika, and Adi Panca Saputra Iskandar. "Analisis Kinerja Fuzzy C-Means (FCM) dan Fuzzy Subtractive (FS) dalam Clustering Data Alumni STMIK STIKOM Indonesia." INFORMAL: Informatics Journal 6, no. 1 (April 29, 2021): 41. http://dx.doi.org/10.19184/isj.v6i1.22077.
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In this study, clustering data on STMIK STIKOM Indonesia alumni using the Fuzzy C-Means and Fuzzy Subtractive methods. The method used to test the validity of the cluster is the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index. Clustering is carried out with the aim of finding hidden patterns or information from a fairly large data set, considering that so far the alumni data at STMIK STIKOM Indonesia have not undergone a data mining process. The results of measuring cluster validity using the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index, the Fuzzy C-Means Clustering algorithm has a higher level of validity than the Fuzzy Subtractive Clustering algorithm so it can be said that the Fuzzy C-Means algorithm performs the cluster process better than with the Fuzzy Subtractive method in clustering alumni data. The number of clusters that have the best fitness value / the most optimal number of clusters based on the CE and MPC validity index is 5 clusters. The cluster that has the best characteristics is the 1st cluster which has 514 members (36.82% of the total alumni). With the characteristics of having an average GPA of 3.3617, the average study period is 7.8102 semesters and an average TA work period of 4.9596 months.
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Aruna Kumar, S. V., and B. S. Harish. "A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation." Journal of Intelligent Systems 27, no. 4 (October 25, 2018): 593–607. http://dx.doi.org/10.1515/jisys-2016-0241.
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Abstract This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.
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Rini, Dian Candra. "Klasifikasi Sinyal EEG Menggunakan Metode Fuzzy C-Means Clustering (FCM) Dan Adaptive Neighborhood Modified Backpropagation (ANMBP)." Jurnal Matematika "MANTIK" 1, no. 1 (November 18, 2015): 31. http://dx.doi.org/10.15642/mantik.2015.1.1.31-36.
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Instrumen EEG (electroencephalography) merupakan suatu instrumen yang digunakan sebagai perekam aktivitas otak dengan memperlihatkan gelombang otak. Prinsip kerja EEG adalah dengan mendeteksi perubahan muatan secara tiba-tiba dari sel neuron yang ditandai dengan adanya interictal spike-and-wave pada hasil EEG (electroencephalogram). Terdapat suatu data set sinyal EEG, direkam pada sukarelawan normal dan epilepsi. Pada penelitian ini dengan menggunakan data tersebut akan dilakukan suatu sistem klasifikasi sinyal EEG dengan berdasar pada kondisi normal dan epilepsi. Klasifikasi sinyal EEG menggunakan Metode Adaptive Neighborhood Base Modified Backpropagation (ANMBP). Hasil ekstraksi fitur dari sinyal EEG dengan menggunakan metode Fuzzy C-Means (FCM) Clustering, dimana proses awalnya melalui dekomposisi wavelet menggunakan Discrete Wavelet Transform (DWT) dengan level 2 didapatkan 3 koefisien wavelet kemudian pada masing masing koefisien tersebut di clustering menggunakan FCM dengan 2 cluster sehingga menghasilkan 6 fitur yang akan menjadi vektor fitur. Dari vektor fitur tersebut digunakan sebagai inputan untuk dilakukan proses klasifikasi dengan menggunakan metode ANMBP. Hasil sistem sementara didapatkan recognition rate sebesar 74.37%.
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Yanto, Iwan Tri Riyadi, Ririn Setiyowati, Nursyiva Irsalinda, Rasyidah, and Tri Lestari. "Laying Chicken Algorithm (LCA) Based For Clustering." JOIV : International Journal on Informatics Visualization 4, no. 4 (December 18, 2020): 208. http://dx.doi.org/10.30630/joiv.4.4.467.
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Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.
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Hu, Yu Jen, Yuh Hua Hu, and Jyh Bin Ke. "The Modified DNA Identification Classification on Fuzzy Relation." Applied Mechanics and Materials 48-49 (February 2011): 1275–81. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1275.
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We proposed a categorized method of DNA sequences matrix by FCM (fuzzy cluster means). FCM avoided the errors caused by the reduction of dimensions. It further reached comprehensive machine learning. In our experiment, there are 40 training data which are artificial samples, and we verify the proposed method with 182 natural DNA sequences. The result showed the proposed method enhanced the accuracy of the classification of genes from 76% to 93%.
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Paasche, Hendrik, Jens Tronicke, and Peter Dietrich. "Automated integration of partially colocated models: Subsurface zonation using a modified fuzzy c -means cluster analysis algorithm." GEOPHYSICS 75, no. 3 (May 2010): P11—P22. http://dx.doi.org/10.1190/1.3374411.
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Partitioning cluster analyses are powerful tools for rapidly and objectively exploring and characterizing disparate geophysical databases with unknown interrelations between individual data sets or models. Despite its high potential to objectively extract the dominant structural information from suites of disparate geophysical data sets or models, cluster-analysis techniques are underused when analyzing geophysical data or models. This is due to the following limitations regarding the applicability of standard partitioning cluster algorithms to geophysical databases: The considered survey or model area must be fully covered by all data sets; cluster algorithms classify data in a multidimensional parameter space while ignoring spatial information present in the databases and are therefore sensitive to high-frequency spatial noise (outliers); and standard cluster algorithms such asfuzzy [Formula: see text]-means (FCM) or crisp [Formula: see text]-means classify data in an unsupervised manner, potentially ignoring expert knowledge additionally available to the experienced human interpreter. We address all of these issues by considering recent modifications to the standard FCM cluster algorithm to tolerate incomplete databases, i.e., survey or model areas not covered by all available data sets, and to consider spatial information present in the database. We have evaluated the regularized missing-value FCM cluster algorithm in a synthetic study and applied it to a database comprising partially colocated crosshole tomographic P- and S-wave-velocity models. Additionally, we were able to demonstrate how further expert knowledge can be incorporated in the cluster analysis to obtain a multiparameter geophysical model to objectively outline the dominant subsurface units, explaining all available geoscientific information.
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Zhu, Jingxing, Feng Wang, and Hongjian You. "SAR Image Segmentation by Efficient Fuzzy C-Means Framework with Adaptive Generalized Likelihood Ratio Nonlocal Spatial Information Embedded." Remote Sensing 14, no. 7 (March 28, 2022): 1621. http://dx.doi.org/10.3390/rs14071621.
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The existence of multiplicative noise in synthetic aperture radar (SAR) images makes SAR segmentation by fuzzy c-means (FCM) a challenging task. To cope with speckle noise, we first propose an unsupervised FCM with embedding log-transformed Bayesian non-local spatial information (LBNL_FCM). This non-local information is measured by a modified Bayesian similarity metric which is derived by applying the log-transformed SAR distribution to Bayesian theory. After, we construct the similarity metric of patches as the continued product of corresponding pixel similarity measured by generalized likelihood ratio (GLR) to avoid the undesirable characteristics of log-transformed Bayesian similarity metric. An alternative unsupervised FCM framework named GLR_FCM is then proposed. In both frameworks, an adaptive factor based on the local intensity entropy is employed to balance the original and non-local spatial information. Additionally, the membership degree smoothing and the majority voting idea are integrated as supplementary local information to optimize segmentation. Concerning experiments on simulated SAR images, both frameworks can achieve segmentation accuracy of over 97%. On real SAR images, both unsupervised FCM segmentation frameworks work well on SAR homogeneous segmentation in terms of region consistency and edge preservation.
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Gaber, Tarek, Chin-Shiuh Shieh, Yuh-Chung Lin, and Fatma Masmoudi. "Modified Flower Pollination Algorithm based Resource Management Model for Clustered IoT Network." International Journal of Wireless and Ad Hoc Communication 4, no. 2 (2022): 97–106. http://dx.doi.org/10.54216/ijwac.040205.
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Internet of Things (IoT) is a technological innovation that defined interaction and computation of latest period. The objects of Internet of Things would empower by embedded gadgets whose limited sources has to be managed effectively. IoT usually means a network of devices connected through wireless network and interacts through internet. Resource management, particularly energy management, becomes a serious problem while devising IoT gadgets. Numerous researchers stated that routing and clustering were energy effectual solutions for optimum resource management in IoT setting. This study introduces a Modified Flower Pollination Algorithm based Resource Management (MFPA-RMM) model for Clustered IoT Environment. The presented MFPA-RMM model majorly focuses on the clustering the IoT devices in such a way that the resources are proficiently managed. The MFPA-RMM model is derived based on the fuzzy c-means (FCM) with FPA. The FPA approach is called heuristic algorithm has benefits of global optimization and faster convergence, therefore it was incorporated to FCM system for resolving the advantages and disadvantages of FCM method termed FCM-FPA mechanism. The result analysis of the MFPA-RMM model reported the enhanced performance of the MFPA-RMM model over other well-known techniques like LEACH and TEEN.
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Liu, Lifeng, Sam Zandong Sun, Hongyu Yu, Xingtong Yue, and Dong Zhang. "A modified Fuzzy C-Means (FCM) Clustering algorithm and its application on carbonate fluid identification." Journal of Applied Geophysics 129 (June 2016): 28–35. http://dx.doi.org/10.1016/j.jappgeo.2016.03.027.
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Zhang, Jian, and Ling Shen. "An Improved Fuzzyc-Means Clustering Algorithm Based on Shadowed Sets and PSO." Computational Intelligence and Neuroscience 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/368628.
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To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzyc-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.
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Xu, Yan, Ruizhi Chen, Yu Li, Peng Zhang, Jie Yang, Xuemei Zhao, Mengyun Liu, and Dewen Wu. "Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model." Remote Sensing 11, no. 23 (November 25, 2019): 2772. http://dx.doi.org/10.3390/rs11232772.
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Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.
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Van, Quy Hoang, Huy Tran Van, Huy Ngo Hoang, Tuyet Dao Van, and Sergey Ablameyko. "A modified Efficient Manifold Ranking Algorithm for Large Database Image Retrieval." Nonlinear Phenomena in Complex Systems 23, no. 1 (April 14, 2020): 79–89. http://dx.doi.org/10.33581/1561-4085-2020-23-1-79-89.
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The efficient manifold ranking (EMR) algorithm is used quite effectively in content-based image retrieval (CBIR) for large image databases where images are represented by multiple low-level features to describe about the color, texture and shape. The EMR ranking algorithm requires steps to determine anchor points of the image database by using the k-means hard clustering and the accuracy of the ranking depends strongly on the selected anchor points. This paper describes a new result based on a modified Fuzzy C-Means (FCM) clustering algorithm to select anchor points in the large database in order to increase the efficiency of manifold ranking specially for the large database cases. Experiments have demonstrated the effectiveness of the proposed algorithm for the issue of building an anchor graph, the set of anchor points determined by this novel lvdc-FCM algorithm has actually increased the effective of manifold ranking and the quality of images query results which retrieved of the CBIR.
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Yang, Yang, Ming Li, and Xie Ma. "A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved." Mathematical Problems in Engineering 2020 (October 20, 2020): 1–13. http://dx.doi.org/10.1155/2020/5713137.
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To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.
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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.
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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.
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Suerni, Widya, Memi Nor Hayati, and Rito Goejantoro. "A PENERAPAN METODE SUBTRACTIVE FUZZY C-MEANS PADA TINGKAT PARTISIPASI PENDIDIKAN JENJANG SEKOLAH MENENGAH ATAS/SEDERAJAT DI KABUPATEN/KOTA PULAU KALIMANTAN TAHUN 2018." VARIANCE: Journal of Statistics and Its Applications 2, no. 2 (April 20, 2021): 63–74. http://dx.doi.org/10.30598/variancevol2iss2page63-74.
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Cluster analysis is a data exploration method uses to obtain hidden characteristics by forming data clusters. One of the cluster analysis methods is Subtractive Fuzzy C-Means (SFCM). SFCM is a combination of Subtractive Clustering and Fuzzy C-Means methods. The SFCM method has the advantages of not requiring many iterations and the results obtained are more stable and accurate than the FCM and SC methods. This study aims to determine the result of clustering on the enrollment rate data for Senior High School (SHS) / equivalent. The data used were the enrollment rate data for high school / equivalent level in the Regency / City of Kalimantan Island in 2018 using three variables, namely the Crude Participation Rate (CPR), the School Participation Rate (SPR) and the Net Enrollment Rate (NER). Based on the three validity indices, namely Partition Coefficient Index (PCI) Validity Index, Modified Partition Coefficient Index (MPCI), and Xie & Beni Index (XBI) in the SFCM method, the optimal cluster were two clusters.
Keywords: clustering, education, Subtractive Fuzzy C-Means
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PAPAMARKOS, NIKOS. "DOCUMENT GRAY-SCALE REDUCTION USING A NEURO-FUZZY TECHNIQUE." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 04 (June 2003): 505–27. http://dx.doi.org/10.1142/s0218001403002502.
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This paper proposes a new neuro-fuzzy technique suitable for binarization and gray-scale reduction of digital documents. The proposed approach uses both the image gray-scales and additional local spatial features. Both, gray-scales and local feature values feed a Kohonen Self-Organized Feature Map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define two bilevel classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.
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Chen, Chin Chun. "Using Mahalanobis Clustering Algorithm for College Student Learning Fundamental Mathematics." Advanced Materials Research 476-478 (February 2012): 2129–32. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.2129.
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The popular fuzzy c-means algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Mahalanobis Clustering Algorithm Based on FCM by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the Normalized Mahalanobis Clustering Algorithm Based on FCM.
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Song, Jianhua, and Zhe Zhang. "A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation." Information 10, no. 2 (February 21, 2019): 74. http://dx.doi.org/10.3390/info10020074.
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In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.
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Chen, Yen Sheng, Shao Hsien Chen, and Jeih Jang Liou. "Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification." Applied Mechanics and Materials 182-183 (June 2012): 1998–2002. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1998.
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Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scans of the brain. Manual segmentation is extremely time-consuming and tedious. Therefore, fully automated MS detection methods are being developed which can classify large amounts of MR data, and do not suffer from inter observer variability. In this paper we use standard fuzzy c-means algorithm (FCM) for multi-spectral images to segment patient MRI data. Geodesic Active Contours of Caselles level set is another method we implement to do the brain image segmentation jobs. And then we implement anther modified Fuzzy C-Means algorithm, where we call Bias-Corrected FCM as BCFCM, for bias field estimation for the same thing. Experimental results show the success of all these intelligent techniques for brain medical image segmentation.
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Cen, Junjie, and Yongbo Li. "Deep Learning-Based Anomaly Traffic Detection Method in Cloud Computing Environment." Wireless Communications and Mobile Computing 2022 (March 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/6155925.
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To address the problem of poor detection performance of existing intrusion detection methods in the environment of high-dimensional massive data with uneven class distribution, a deep learning-based anomaly traffic detection method in cloud computing environment is proposed. First, the fuzzy C -means (FCM) algorithm is introduced and is combined with the general regression neural network (GRNN) to cluster the samples to be classified in the original space by FCM. Then, the GRNN model is trained and the center point is updated using the sample closest to the FCM clustering center until a stable cluster center is obtained. The parameters in FCM-GRNN are optimized using the global optimization feature of the modified fruit fly optimization algorithm (MFOA), and the optimal spread value is found using the three-dimensional search method through an iterative search. Finally, experiments are conducted based on the KDD CUP99 dataset, and the results demonstrate that the detection rate (DR) and false alarm rate (FAR) of the proposed FCM-MFOA-GRNN method are 91% and 1.176%, respectively, which are better than those of the comparison methods. Therefore, the proposed method has good anomaly traffic detection ability.
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Karuna, Yepuganti, Saritha Saladi, and Budhaditya Bhattacharyya. "Brain Tissue Classification using PCA with Hybrid Clustering Algorithms." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 536. http://dx.doi.org/10.14419/ijet.v7i2.24.12155.
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Distinct algorithms were developed to segment the MRI images, to satisfy the accuracy in segmenting the regions of the brain. In this paper, we proposed a novel methodology for segmenting the MRI brain images using the clustering techniques. The Modified Fuzzy C-Means (MFCM) algorithm is pooled with the Artificial Bee Colony (ABC) algorithm after denoising images, features are extracted using Principal Component Analysis (PCA) for better results of segmentation. This improves the ability to extract the regions (cluster centres) and cells in the normal and abnormal brain MRI images. The comparative analysis of proposed methodology with existing FCM, ABC algorithms is evaluated in terms of Minkowski score. The proposed MFCM-ABC method is more robust and efficient to hostile noise in images when compared to existing FCM and ABC methods.
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Xu, Guojing, Shiyu Wang, Tian Yang, and Wen Jiang. "A Neutrosophic Approach Based on TOPSIS Method to Image Segmentation." International Journal of Computers Communications & Control 13, no. 6 (November 29, 2018): 1047–61. http://dx.doi.org/10.15837/ijccc.2018.6.3268.
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Neutrosophic set (NS) is a formal framework proposed recently. NS can not only describe the incomplete information in the decision-making system but also depict the uncertainty and inconsistency, so it has applied successfully in several fields such as risk assessment, fuzzy decision and image segmentation. In this paper, a new neutrosophic approach based on TOPSIS method, which can make full use of NS information, is proposed to separate the graphics. Firstly, the image is transformed into the NS domain. Then, two operations, a modified alpha-mean and the beta-enhancement operations are used to enhance image edges and to reduce uncertainty. At last, the segmentation is achieved by the TOPSIS method and the modified fuzzy c-means (FCM). Simulated images and real images are illustrated that the proposed method is more effective and accurate in image segmentation.
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Barrah, Hanane, Abdeljabbar Cherkaoui, and Driss Sarsri. "Robust FCM Algorithm with Local and Gray Information for Image Segmentation." Advances in Fuzzy Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6238295.
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The FCM (fuzzy c-mean) algorithm has been extended and modified in many ways in order to solve the image segmentation problem. However, almost all the extensions require the adjustment of at least one parameter that depends on the image itself. To overcome this problem and provide a robust fuzzy clustering algorithm that is fully free of the empirical parameters and noise type-independent, we propose a new factor that includes the local spatial and the gray level information. Actually, this work provides three extensions of the FCM algorithm that proved their efficiency on synthetic and real images.
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Ananda, Ridho, Atika Ratna Dewi, and Nurlaili Nurlaili. "A COMPARISON OF CLUSTERING BY IMPUTATION AND SPECIAL CLUSTERING ALGORITHMS ON THE REAL INCOMPLETE DATA." Jurnal Ilmu Komputer dan Informasi 13, no. 2 (July 1, 2020): 65–75. http://dx.doi.org/10.21609/jiki.v13i2.818.
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The existence of missing values will really inhibit process of clustering. To overcome it, some of scientists have found several solutions. Both of them are imputation and special clustering algorithms. This paper compared the results of clustering by using them in incomplete data. K-means algorithms was utilized in the imputation data. The algorithms used were distribution free multiple imputation (DFMI), Gabriel eigen (GE), expectation maximization-singular value decomposition (EM-SVD), biplot imputation (BI), four algorithms of modified fuzzy c-means (FCM), k-means soft constraints (KSC), distance estimation strategy fuzzy c-means (DESFCM), k-means soft constraints imputed-observed (KSC-IO). The data used were the 2018 environmental performance index (EPI) and the simulation data. The optimal clustering on the 2018 EPI data would be chosen based on Silhouette index, where previously, it had been tested its capability in simulation dataset. The results showed that Silhouette index have the good capability to validate the clustering results in the incomplete dataset and the optimal clustering in the 2018 EPI dataset was obtained by k-means using BI where the silhouette index and time complexity were 0.613 and 0.063 respectively. Based on the results, k-means by using BI is suggested processing clustering analysis in the 2018 EPI dataset.
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Liu, Xin, Hong-Kun Chen, Bing-Qing Huang, and Yu-Bo Tao. "Optimal Sizing for Wind/PV/Battery System Using Fuzzy c-Means Clustering with Self-Adapted Cluster Number." International Journal of Rotating Machinery 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5142825.
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Integrating wind generation, photovoltaic power, and battery storage to form hybrid power systems has been recognized to be promising in renewable energy development. However, considering the system complexity and uncertainty of renewable energies, such as wind and solar types, it is difficult to obtain practical solutions for these systems. In this paper, optimal sizing for a wind/PV/battery system is realized by trade-offs between technical and economic factors. Firstly, the fuzzy c-means clustering algorithm was modified with self-adapted parameters to extract useful information from historical data. Furthermore, the Markov model is combined to determine the chronological system states of natural resources and load. Finally, a power balance strategy is introduced to guide the optimization process with the genetic algorithm to establish the optimal configuration with minimized cost while guaranteeing reliability and environmental factors. A case of island hybrid power system is analyzed, and the simulation results are compared with the general FCM method and chronological method to validate the effectiveness of the mentioned method.
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Su, Haoliang, Fang Wang, Leying Zhang, and Guiyang Li. "Fuzzy Clustering Algorithm-Segmented MRI Images in Analysis of Effects of Mental Imagery on Neurorehabilitation of Stroke Patients." Scientific Programming 2021 (July 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/9945153.
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The study focused on the automatic segmentation of Magnetic Resonance Imaging (MRI) images of stroke patients and the therapeutic effects of Mental Imagery on motor and neurological functions after stroke. First, the traditional fuzzy c-means (FCM) algorithm was optimized, and the optimized one was defined as filter-based FCM (FBFCM). 62 stroke patients were selected as the research subjects and randomly divided into the experimental group and the control group. The control group accepted the conventional rehabilitation training, and the experimental group accepted Mental Imagery on the basis of the control group. They all had the MRI examination, and their brain MRI images were segmented by the FBFCM algorithm. The MRI images before and after treatment were analyzed to evaluate the therapeutic effects of Mental Imagery on patients with motor and nerve dysfunction after stroke. The results showed that the segmentation coefficient of the FBFCM algorithm was 0.9315 and the segmentation entropy was 0.1098, which were significantly different from those of the traditional fuzzy c-means (FCM) algorithm. ( P < 0.05 ), suggesting that the FBFCM algorithm had good segmentation effects on brain MRI images of stroke patients. After Mental Imagery, it was found that the patient’s Function Independent Measure (FIM) score was 99.04 ± 8.19, the Modified Barthel Index (MBI) score was 51.29 ± 4.35, the Fugl-Meyer (FMA) score was 61.01 ± 4.16, the neurological deficit degree in stroke (NFDS) score was 11.48 ± 2.01, the NIH Stroke Scale (NIHSS) score was 10.36 ± 1.69, and the clinical effective rate was 87.1%, all significantly different from those of the conventional rehabilitation training group ( P < 0.05 ). Additionally, the brain area activated by Mental Imagery was more extensive. In conclusion, the FBFCM algorithm demonstrates superb capabilities in segmenting MRI images of stroke patients and is worth promotion in clinic. Mental Imagery can promote the neurological rehabilitation of patients by activating relevant brain areas of patients.
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Yih, Jeng Ming. "Supervised Clustering Algorithm for University Student Learning Algebra." Advanced Materials Research 542-543 (June 2012): 1376–79. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1376.
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The popular fuzzy c-means algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. In this paper, an improved Normalized Supervised Clustering Algorithm Based on FCM by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the Normalized Supervised Clustering Algorithm.
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Alzahrani, Ali, Theyazn H. H. Aldhyani, Saleh Nagi Alsubari, and Ans D. Alghamdi. "Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model." Security and Communication Networks 2022 (June 20, 2022): 1–14. http://dx.doi.org/10.1155/2022/3553622.
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Industry 4.0, also known as the Internet of Things, is a concept that encompasses the joint applicability of operation, the Internet, and information technologies to expand the efficiency expectation of automation to include green and flexible processes and innovative products and services. Industrial network infrastructures must be modified to accommodate extra traffic from a variety of technologies in order to achieve this integration. In order to successfully implement cutting-edge wireless technologies, high-quality service (QoS) must be provided to end users. It is thus important to keep an eye on the functioning of the whole network without impacting base station throughput. Improved network performance is constantly needed, even for already-deployed cellular networks, such as the 4th generation (4G) and 3rd generation (3G). For the purpose of forecasting network traffic, an integrated model based on the long short-term memory (LSTM) model was used to combine clustering rough k-means (RKM) and fuzzy c-means (FCM). Clustering granules derived from FCM and RKM were also utilized to examine the network data for each calendar year. The novelty of our proposed model is the integration of the prediction and forecasting results obtained using existing prediction models with centroids of clusters. The WIDE backbone network’s live network traffic statistics were used to evaluate the proposed solution. The integrated model’s outcomes were assessed using a variety of statistical markers, including mean square error (MSE), root mean square error (RMSE), and standard error. The suggested technique was able to provide findings that were very accurate. The prediction error of LSTM with FCM was less on the basis of the MSE of 0.00783 and RMSE of 0.0885 at the training phase, where the prediction values of LSTM with the RKM had an MSE of 0.00564 and RMSE of 0.7511. Finally, the suggested model may substantially increase the prediction accuracy attained using FCM and RKM clustering.
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Cao, Shan, Yu Qi Ji, and Guang Fei Geng. "Research on Unequal Grouping of Parallel Compensation Capacity in Substation." Applied Mechanics and Materials 556-562 (May 2014): 1636–42. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.1636.
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For the problem of unequal grouping of parallel compensation capacity in substation, this paper proposes a new optimization method based on curve segmentation and clustering. Firstly, calculate reactive power demand curve by transformer parameters and load curve, partition this curve into several segments. Then cluster these segmentation results into K clusters by using modified FCM (fuzzy C-means clustering) algorithm, in which K means the number of capacitor groups. Take the difference between two adjacent cluster centers as the capacity of each group. Furthermore, study the relationship between segment number and grouping centers in order to get the steady grouping results. After the grouping plan is determined, take the nine-area figure as control strategy. Finally, simulating with an equivalent practical power grid and load profile, the results show both the availability and rationality of this method that power loss is less and power factor is higher compared with the equal grouping method when capacitor is divided into 3 groups.
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Hamad, Sumaya, Khattab Alheeti, Yossra Ali, and Shaimaa Shaker. "Clustering and Analysis of Dynamic Ad Hoc Network Nodes Movement Based on FCM Algorithm." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 47. http://dx.doi.org/10.3991/ijoe.v16i12.16067.
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<p><strong>Abstract—</strong> Clustering is a major exploratory data mining activity, and a popular statistical data analysis technique used in many fields. Cluster analysis generally speaking isn't just an automated function, but rather reiterated information exploration procedure or multipurpose dynamic optimisation Comprising trial and error. Parameters for pre-processing and modeling data frequently need to be modified until the output hits the desired properties. -Data points in fuzzy clustering may probably belong to several clusters. Each Data Point is assigned membership grades. Such grades of membership reflect the degree to which data points belong to each cluster. The Fuzzy C-means clustering (FCM) algorithm is among the most widely used fuzzy clustering algorithms. In this paper We use this method to find typological analysis for dynamic Ad Hoc network nodes movement and demonstrate that we can achieve good performance of fuzziness on a simulated data set of dynamic ad hoc network nodes (DANET) and How to use this principle to formulate node clustering as a partitioning problem. Cluster analysis aims at grouping a collection of nodes into clusters in such a way that nodes seeing a high degree of correlation within the same cluster, whereas nodes members of various clusters are extremely dissimilar in nature. The FCM algorithm is used for implementation and evaluation the simulated data set using NS2 simulator with optimized AODV protocol. The results from the algorithm 's application show the technique achieved the maximum values of stability for both cluster centers and nodes (98.41 %, 99.99 %) respectively.<strong></strong></p>
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Gharieb, R. R., G. Gendy, and H. Selim. "A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 04 (December 13, 2017): 1850012. http://dx.doi.org/10.1142/s021800141850012x.
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In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.
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Wang, Degang, Wenyan Song, Witold Pedrycz, and Lili Cai. "An integrated neural network with nonlinear output structure for interval-valued data." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 673–83. http://dx.doi.org/10.3233/jifs-200500.
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In this paper, an integrated model combining interval deep belief network (IDBN) and neural network with nonlinear weights, called IDBN-NN, is proposed for interval-valued data modeling. Firstly, the IDBN with variable learning rate is designed to initialize parameters of each sub-model. Based on a modified contrastive divergence algorithm the least square method is adopted to identify the coefficients of nonlinear weights in the output layer. Then, to improve the modeling accuracy, the Fuzzy C-Means (FCM) method and the Particle Swarm Optimization (PSO) algorithm are applied to tune the weights of sub-models. Though each sub-model can capture the nonlinear feature of the original system, by intersecting cut sets the synthesizing modeling scheme can further improve the performance of the proposed model. Some numerical examples show that the IDBN-NN with nonlinear output structure can achieve higher accuracy than some interval-valued data modeling methods.
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Mishra, Satyasis, Demissie J. Gelmecha, Ram S. Singh, Davinder Singh Rathee, and T. Gopikrishna. "HYBRID WCA–SCA AND MODIFIED FRFCM TECHNIQUE FOR ENHANCEMENT AND SEGMENTATION OF BRAIN TUMOR FROM MAGNETIC RESONANCE IMAGES." Biomedical Engineering: Applications, Basis and Communications 33, no. 03 (March 3, 2021): 2150017. http://dx.doi.org/10.4015/s1016237221500174.
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Enhancement of image plays an important and vital role in preprocessing the magnetic resonance images (MRI). At the same time, image segmentation techniques are also essential to detect and remove the noise to enhance the quality of MRI to detect the infected regions of the brain tumor. This paper presents a novel image enhancement technique for preprocessing of brain tumor MRI by hybridizing the Water Cycle Algorithm (WCA) and Sine Cosine Algorithm (SCA). The WCA is based on the process of water cycle in rivers and streams flow in the ocean whereas the SCA follows the cyclic form of sine and cosine trigonometric functions, which permits a search agent to be transposed around the desired solution. In fact, the Fuzzy [Formula: see text] means-based segmentation algorithms have proved their ability in automatic detection of the tumor and help doctors and radiologist to diagnose the type of tumor from the MRI, but, some of the FCM-based algorithms fail to remove the required amount of noise from the MRI which restrict doctors to have better segmentation accuracy. A modified fast and robust FCM (MFRFCM) segmentation technique has been proposed to sharpen and remove noise from MRI to detect the brain tumor to have improved accuracy. In this research work, Dataset-255 is considered from the Harvard medical school. The results from the proposed hybrid WCA-SCA technique are compared with WCA, SCA and comparison results are presented. The hybrid WCA+SCA image enhancement technique attains an accuracy of 99.25% for benign tumor and 98.52% for malignant tumor. Further, the results of modified Fast and Robust FCM (MFRFCM) segmentation results are compared with the conventional FCM-based segmentation algorithms. It is observed that the proposed hybrid WCA-SCA image enhancement technique and modified FRFCM Segmentation outperform in terms of computational time and performance accuracy in contrast to the other algorithms.
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Chen, Ji Lin, Jin Qian, Zhong Zhi Tong, and Yuan Long Hou. "A Revised Clustering Algorithm Based RBF Neural Network Approach for Modeling of an Electro-Hydraulic Servo System." Applied Mechanics and Materials 157-158 (February 2012): 1595–600. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1595.
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The paper presents an approach to model the electro-hydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural network, a revised clustering method is used to train the hidden node centers of the neural network, in which the subtractive clustering(SC) algorithm was used to determine the initial centers and the fuzzy C – Means(FCM) clustering algorithm to further determined the centers data set. The spread factors and the weights of the neural network are calculated by the modified recursive least squares (MRLS) algorithm for relieving computational burden. The proposed algorithm is verified by its application to the modeling of an electro-hydraulic system, simulation and experiment results clearly indicate the obtained RBF network can model the electro-hydraulic system satisfactorily and comparison results also show that the proposed algorithm performs better than the other methods.
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Anggoro, Faisal, Rezzy Eko Caraka, Fajar Agung Prasetyo, Muthia Ramadhani, Prana Ugiana Gio, Rung-Ching Chen, and Bens Pardamean. "Revisiting Cluster Vulnerabilities towards Information and Communication Technologies in the Eastern Island of Indonesia Using Fuzzy C Means." Sustainability 14, no. 6 (March 15, 2022): 3428. http://dx.doi.org/10.3390/su14063428.
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Design/methodology/approach: In the present digital era, technology infrastructure plays an important role in the development of digital literacy in various sectors that can provide various important information on a large scale. Purpose: The use of information and communication technology (ICT) in Indonesia in the last five years has shown a massive development of ICT indicators. The population using the internet also experienced an increase during the period 2016–2020, as indicated by the increasing percentage of the population accessing the internet in 2016 from around 25.37 percent to 53.73 percent in 2020. This study led to a review of the level of ICT vulnerability in eastern Indonesia through a machine learning-based cluster analysis approach. Implications: Data were collected in this study from Badan Pusat Statistik (BPS) through SUSENAS to obtain an overview of the socioeconomic level and SAKERNAS to capture the employment side. This study uses 15 variables based on aspects of business vulnerability covering 174 districts/cities. Practical implications: Cluster analysis using Fuzzy C Means (FCM) was used to obtain a profile of ICT level vulnerability in eastern Indonesia by selecting the best model. The best model is obtained by selecting the validation value such as Silhouette Index, Partition Entropy, Partition Coefficient, and Modified Partition Coefficient. Social implication: For some areas with a very high level of vulnerability, special attention is needed for the central or local government to support the improvement of information technology through careful planning. Socio-economic and occupational aspects have been reflected in this very vulnerable cluster, and the impact of the increase in ICT will provide a positive value for community development. Originality/value: From the modelling results, the best cluster model is two clusters, which are categorized as high vulnerability and low vulnerability. For each cluster member who has a similarity or proximity to each other, there will be one cluster member.
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Hu, Chunchun, and Jean-Claude Thill. "Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories." ISPRS International Journal of Geo-Information 8, no. 7 (June 27, 2019): 295. http://dx.doi.org/10.3390/ijgi8070295.
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Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis.
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Supartha, I. Kadek Dwi Gandika, Made Sudarma, and Dewa Made Wiharta. "Sistem Informasi Geografis Pemetaan Persebaran Alumni dengan Analisa Clustering." Majalah Ilmiah Teknologi Elektro 17, no. 3 (December 5, 2018): 377. http://dx.doi.org/10.24843/mite.2018.v17i03.p12.
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STMIK STIKOM Indonesia (STIKI Indonesia) memiliki data alumni yang cukup banyak tetapi data tersebut tidak diolah lebih lanjut untuk menjadi informasi yang lebih berguna. STIKI Indonesia juga kurang mengetahui informasi persebaran alumni di dunia kerja. Untuk mengatasi permasalah tersebut dapat memanfaatkan teknologi Sistem Informasi Geografis (SIG) dan data mining. SIG memiliki kemampuan untuk menyajikan informasi dalam bentuk grafis dan data mining bisa mengekstraksi pola yang tersembunyi dari database besar. Clustering dilakukan pada data alumni dengan atribut bidang pekerjaan, Indeks Prestasi Komulatif (IPK), lama study dan lama pengerjaan tugas akhir. Metode yang digunakan yaitu Fuzzy C-Means (FCM) dan untuk pengukuran validitas cluster menggunakan Modified Partition Coefficient (MPC) dan Classification Entropy (CE). Hasil pengujian menunjukkan bahwa jumlah cluster yang paling optimal adalah 7 cluster dan cluster yang memiliki karakteristik terbaik adalah cluster ke 1 yang jumlah anggotanya 49 (9,3155% dari jumlah keseluruhan alumni), jumlah ini masih sangat kecil jika dibandingkan dengan total keseluruhan jumlah alumni. Pengujian menggunakan metode black box pada Sistem Informasi Geografis Pemetaan Persebaran Alumni dengan Analisa Clustering didapatkan hasil bahwa semua modul dalam sistem telah berfungsi dengan baik.
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Sheela, C. Jaspin Jeba, and G. Suganthi. "Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm." Multimedia Tools and Applications 79, no. 25-26 (February 20, 2020): 17483–96. http://dx.doi.org/10.1007/s11042-020-08636-9.
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Br Bangun, Desy Milbina, Syahril Efendi, and Rahmat W. Sembiring. "Analysis of Data classification accuracy using ANFIS algorithm modification with K-Medoids clustering." SinkrOn 7, no. 3 (August 13, 2022): 2080–88. http://dx.doi.org/10.33395/sinkron.v7i3.11610.
Full textAbstract:
The ANFIS algorithm is a technique in data mining that can be used for the data classification process. The ANFIS algorithm still has weaknesses, especially in determining the initial parameters for the network training process. Thus, an additional algorithm or modification is needed for the determination of these parameters. In this study, a clustering method will be proposed, namely K-Medoids Clustering as an additional method to the ANFIS algorithm. Basically, the ANFIS algorithm uses the FCM (Fuzzy C-Means Clustering) algorithm for the initial initialization of network parameters. The use of this method can cause local minima problems, where the clustering results obtained are not optimal because the pseudo-partition matrix generation process is carried out randomly. The matrix value will determine the initial parameter value in the ANFIS algorithm used in the first layer. Based on the research that has been done, it can be concluded that the accuracy of data classification using the ANFIS algorithm which has been modified with the proposed method provides a fairly good influence in conducting training and classification testing. The increase that occurs in the proposed method is 0.73% for the average training accuracy and an increase of 0.66% for the average testing accuracy.