Relevant bibliographies by topics / Modified fuzzy c-means (FCM)

Academic literature on the topic 'Modified fuzzy c-means (FCM)'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Modified fuzzy c-means (FCM).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Modified fuzzy c-means (FCM)"

1

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Modified fuzzy c-means (FCM)"

1

Hong, Sui. "Experiments with K-Means, Fuzzy c-Means and Approaches to Choose K and C." Honors in the Major Thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1224.

Full text
Abstract:
This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf
Bachelors
Engineering and Computer Science
Computer Engineering
APA, Harvard, Vancouver, ISO, and other styles
2

Naik, Vaibhav C. "Fuzzy C-means clustering approach to design a warehouse layout." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000437.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Muna, Izza Hasanul. "Modely a metody pro svozové problému v logistice." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-401586.

Full text
Abstract:
The thesis focuses on how to optimize vehicle routes for distributing logistics. This vehicle route optimization is known as a vehicle routing problem (VRP). The VRP has been extended in numerous directions for instance by some variations that can be combined. One of the extension forms of VRP is a capacitated VRP with stochastics demands (CVRPSD), where the vehicle capacity limit has a non-zero probability of being violated on any route. So, a failure to satisfy the amount of demand can appear. A strategy is required for updating the routes in case of such an event. This strategy is called as recourse action in the thesis. The main objective of the research is how to design the model of CVRPSD and find the optimal solution. The EEV (Expected Effective Value) and FCM (Fuzzy C-Means) – TSP (Travelling Salesman Problem) approaches are described and used to solve CVRPSD. Results have confirmed that the EEV approach has given a better performance than FCM-TSP for solving CVRPSD in small instances. But EEV has disadvantage, that the EEV is not capable to solve big instances in an acceptable running time because of complexity of the problem. In the real situation, the FCM –TSP approach is more suitable for implementations than the EEV because the FCM – TSP can find the solution in a shorter time. The disadvantage of this algorithm is that the computational time depends on the number of customers in a cluster.
APA, Harvard, Vancouver, ISO, and other styles
4

Ataeian, Seyed Mohsen, and Mehrnaz Jaberi Darbandi. "Analysis of Quality of Experience by applying Fuzzy logic : A study on response time." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5742.

Full text
Abstract:
To be successful in today's competitive market, service providers should look at user's satisfaction as a critical key. In order to gain a better understanding of customers' expectations, a proper evaluations which considers intrinsic characteristics of perceived quality of service is needed. Due to the subjective nature of quality, the vagueness of human judgment and the uncertainty about the degree of users' linguistic satisfaction, fuzziness is associated with quality of experience. Considering the capability of Fuzzy logic in dealing with imprecision and qualitative knowledge, it would be wise to apply it as a powerful mathematical tool for analyzing the quality of experience (QoE). This thesis proposes a fuzzy procedure to evaluate the quality of experience. In our proposed methodology, we provide a fuzzy relationship between QoE and Quality of Service (QoS) parameters. To identify this fuzzy relationship a new term called Fuzzi ed Opinion Score (FOS) representing a fuzzy quality scale is introduced. A fuzzy data mining method is applied to construct the required number of fuzzy sets. Then, the appropriate membership functions describing fuzzy sets are modeled and compared with each other. The proposed methodology will assist service providers for better decision-making and resource management.
APA, Harvard, Vancouver, ISO, and other styles
5

(14030507), Deepani B. Guruge. "Effective document clustering system for search engines." Thesis, 2008. https://figshare.com/articles/thesis/Effective_document_clustering_system_for_search_engines/21433218.

Full text
Abstract:

People use web search engines to fill a wide variety of navigational, informational and transactional needs. However, current major search engines on the web retrieve a large number of documents of which only a small fraction are relevant to the user query. The user then has to manually search for relevant documents by traversing a topic hierarchy, into which a collection is categorised. As more information becomes available, it becomes a time consuming task to search for required relevant information.

This research develops an effective tool, the web document clustering (WDC) system, to cluster, and then rank, the output data obtained from queries submitted to a search engine, into three pre-defined fuzzy clusters. Namely closely related, related and not related. Documents in closely related and related documents are ranked based on their context.

The WDC output has been compared against document clustering results from the Google, Vivisimo and Dogpile systems as these where considered the best at the fourth Search Engine Awards [24]. Test data was from standard document sets, such as the TREC-8 [118] data files and the Iris database [38], or 3 from test text retrieval tasks, "Latex", "Genetic Algorithms" and "Evolutionary Algorithms". Our proposed system had as good as, or better results, than that obtained by these other systems. We have shown that the proposed system can effectively and efficiently locate closely related, related and not related, documents among the retrieved document set for queries submitted to a search engine.

We developed a methodology to supply the user with a list of keywords filtered from the initial search result set to further refine the search. Again we tested our clustering results against the Google, Vivisimo and Dogpile systems. In all cases we have found that our WDC performs as well as, or better than these systems.

The contributions of this research are:

  1. A post-retrieval fuzzy document clustering algorithm that groups documents into closely related, related and not related clusters. This algorithm uses modified fuzzy c-means (FCM) algorithm to cluter documents into predefined intelligent fuzzy clusters and this approach has not been used before.
  2. The fuzzy WDC system satisfies the user's information need as far as possible by allowing the user to reformulate the initial query. The system prepares an initial word list by selecting a few characteristics terms of high frequency from the first twenty documents in the initial search engine output. The user is then able to use these terms to input a secondary query. The WDC system then creates a second word list, or the context of the user query (COQ), from the closely related documents to provide training data to refine the search. Documents containing words with high frequency from the training list, based on a pre-defined threshold value, are then presented to the user to refine the search by reformulating the query. In this way the context of the user query is built, enabling the user to learn from the keyword list. This approach is not available in current search engine technology.
  3. A number of modifications were made to the FCM algorithm to improve its performance in web document clustering. A factor swkq is introduced into the membership function as a measure of the amount of overlaping between the components of the feature vector and the cluster prototype. As the FCM algorithm is greatly affected by the values used to initialise the components of cluster prototypes a machine learning approach, using an Evolutionary Algorithm, was used to resolve the initialisation problem.
  4. Experimental results indicate that the WDC system outperformed Google, Dogpile and the Vivisimo search engines. The post-retrieval fuzzy web document clustering algorithm designed in this research improves the precision of web searches and it also contributes to the knowledge of document retrieval using fuzzy logic.
  5. A relational data model was used to automatically store data output from the search engine off-line. This takes the processing of data of the Internet off-line, saving resources and making better use of the local CPU.
  6. This algorithm uses Latent Semantic Indexing (LSI) to rank documents in the closely related and related clusters. Using LSI to rank document is wellknown, however, we are the first to apply it in the context of ranking closely related documents by using COQ to form the term x document matrix in LSI, to obtain better ranking results.
  7. Adjustments based on document size are proposed for dealing with problems associated with varying document size in the retrieved documents and the effect this has on cluster analysis.
APA, Harvard, Vancouver, ISO, and other styles
6

Guo, Jen-Der, and 郭建得. "Heartbeat Case Determination Using the Fuzzy C-Means (FCM) Method on ECG Signals." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/58048189342571486634.

Full text
Abstract:
碩士
健行科技大學
電子工程系碩士班
103
This paper presents a simple and effective electrocardiogram (ECG) heartbeat species identification method, which includes: (1) ECG signal pre-processor: the aim is to enlarge the body taken from the patient to the ECG signal, and do all kinds of miscellaneous information removal process; (2) ECG signal transmission: the post-processing of the ECG signal to Wi-Fi wireless communication technology is transferred to the receiver; and (3) calculation of the original feature points feature value: according to the received Wi-Fi receiver ECG signal to the middle of the QRS complex, P T wave spread position, a characteristic feature of the original value of each point; selection (4) the main features of the point: the principal component analysis (Principal Component Analysis; PCA) to select the main feature points the aim is to reduce the time the heartbeat species identification; (5) the heartbeat species identification: fuzzy clustering average (Fuzzy C-Means) method to identify the type of cardiac patients heartbeat, the heartbeat of this paper can identify five species occur more frequently, contain normal heartbeat (NORM) and four kinds of abnormal heartbeat. Four kinds of irregular heartbeat were: a left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC), and atrial premature contraction (APC) and so on. Finally, this paper MIT-BIH arrhythmia database related files to assess the effectiveness of the proposed method, the actual testing, identification heartbeat category NORM, LBBB, RBBB, VPC, and APC''s Se were up 98.28%, 90.35 %, 86.97%, 92.19%, and 94.36%. The total average rate of correct judgment TCA was 93.57%.
APA, Harvard, Vancouver, ISO, and other styles
7

Qu, Zhong-Zheng, and 瞿忠正. "VQ-based image compression using modified fuzzy C-Means method." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/01706199175015486439.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Choi, Yunsik. "Mapping continuous soil properties using fuzzy C-means (FCM) clustering on soil similarity vectors generated from solim." 2005. http://catalog.hathitrust.org/api/volumes/oclc/62501656.html.

Full text
Abstract:
Thesis (M.S.)--University of Wisconsin--Madison, 2005.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 71-76).
APA, Harvard, Vancouver, ISO, and other styles
9

Huang, Chia-tai, and 黃家泰. "Blind Equalization of Inter-Symbol Interference Based on Modified Fuzzy-C-Means Algorithms." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55257174711396041374.

Full text
Abstract:
碩士
國立雲林科技大學
電子與資訊工程研究所
94
Abstract – Usually, adaptive equalizer to channel conditions is obtained by inserting a known sequence within the data set: the deconvolution of this sequence should allow the receiver to extract enough knowledge about the channel conditions to tune the filler weights and invert the channel transfer function. Blind equalizers on the contrary, extract their knowledge directly from the channel output without using any training input data. In this thesis, the fuzzy-c-means (FCM) clustering algorithm is used to achieve joint equalization and demodulation of a QAM signal affected by intersymbol interference (ISI) in a blind mode without insertion of a training sequence in the data stream. To improve decision quality, the fuzzy possibilistic c-means (FPCM) algorithm and possibilistic fuzzy c-means algorithm (PFCM) are applied into the blind equalization framework. Furthermore, an accumulative cluster centers concept is proposed to compact the performance degradation of the clustering-based blind equalization when the frame size of data is too small. Computer simulation demonstrates that the proposed algorithms can achieve performance improvement in the system.
APA, Harvard, Vancouver, ISO, and other styles
10

yu, Chin, and 游縉. "An Inferior Quality Images Segmentation Algorithm Based on the Modified Fuzzy C-Means Method." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4y38me.

Full text
Abstract:
碩士
國立臺北科技大學
自動化科技研究所
106
In this thesis, an images segmentation algorithm based on the modified fuzzy C-means method is proposed to segment the image with low contrast, underexposure or noises accurately. Firstly, based on the histogram equalization, a novel image fusion method is proposed to modify the image with over enhanced and distortion. In addition, we adopt the modified noise filter algorithm to eliminate noises and the details of the image is also preserved. Moreover, in order to segment the image accurately, the proposed modified fuzzy C-means algorithm is utilized to classify the filtered image. Lastly, the experiment results illustrate that the proposed images segmentation algorithm can segment the inferior image effectively.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Modified fuzzy c-means (FCM)"

1

Zhang, David, Wangmeng Zuo, and Peng Wang. "Modified Gaussian Models and Fuzzy C-Means." In Computational Pulse Signal Analysis, 231–46. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4044-3_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Singh, Nisha, Vivek Srivastava, and Komal. "Iris Data Classification Using Modified Fuzzy C Means." In Computational Intelligence: Theories, Applications and Future Directions - Volume I, 345–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Yang, Yong, Chongxun Zheng, and Pan Lin. "Image Thresholding via a Modified Fuzzy C-Means Algorithm." In Lecture Notes in Computer Science, 589–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30463-0_74.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Yanling, and Gang Li. "Fuzzy C-Means Cluster Segmentation Algorithm Based on Modified Membership." In Advances in Neural Networks – ISNN 2009, 135–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Pi, Dechang, Xiaolin Qin, and Peisen Yuan. "A Modified Fuzzy C-Means Algorithm for Association Rules Clustering." In Lecture Notes in Computer Science, 1093–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_137.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lázaro, Jesús, Jagoba Arias, José L. Martín, and Carlos Cuadrado. "Modified Fuzzy C-Means Clustering Algorithm for Real-Time Applications." In Field Programmable Logic and Application, 1087–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45234-8_126.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Nayak, Janmenjoy, Bighnaraj Naik, and H. S. Behera. "Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014." In Computational Intelligence in Data Mining - Volume 2, 133–49. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bilenia, Aniket, Daksh Sharma, Himanshu Raj, Rahul Raman, and Mahua Bhattacharya. "Brain Tumor Segmentation with Skull Stripping and Modified Fuzzy C-Means." In Information and Communication Technology for Intelligent Systems, 229–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1742-2_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kobashi, Syoji, Yutaka Hata, Yuri T. Kitamura, Toshiaki Hayakata, and Toshio Yanagida. "Brain State Recognition Using Fuzzy C-Means (FCM) Clustering with Near Infrared Spectroscopy (NIRS)." In Computational Intelligence. Theory and Applications, 124–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Arumugam, Sajeev Ram, Bharath Bhushan, Monika Arya, Oswalt Manoj, and Syed Muzamil Basha. "Lung Cancer Detection Using Modified Fuzzy C-Means Clustering and Adaptive Neuro-Fuzzy Network." In Lecture Notes in Electrical Engineering, 733–42. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4831-2_60.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Modified fuzzy c-means (FCM)"

1

Suprihatin, Iwan Tri Riyadi Yanto, Nursyiva Irsalinda, Tuti Purwaningsih, Haviluddin, and Aji Prasetya Wibawa. "A performance of modified fuzzy C-means (FCM) and chicken swarm optimization (CSO)." In 2017 3rd International Conference on Science in Information Technology (ICSITech). IEEE, 2017. http://dx.doi.org/10.1109/icsitech.2017.8257105.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, L. F., Z. D. Sun, X. Y. Zhou, J. F. Han, B. Jing, Y. Y. Pan, H. T. Zhao, and Y. Neng. "A New Algorithm of Modified Fuzzy C Means Clustering (FCM) and the Prediction of Carbonate Fluid." In 76th EAGE Conference and Exhibition 2014. Netherlands: EAGE Publications BV, 2014. http://dx.doi.org/10.3997/2214-4609.20140801.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Nasution, Bahrul Ilmi, and Robert Kurniawan. "Robustness of classical fuzzy C-means (FCM)." In 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018. http://dx.doi.org/10.1109/icoiact.2018.8350729.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Saad, Mohamed Fadhel, and Adel M. Alimi. "Improved Modified Suppressed Fuzzy C-Means." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586754.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Pei, Jihong, Xuan Yang, Xinbo Gao, and Weixing Xie. "Weighting exponent m in fuzzy C-means (FCM) clustering algorithm." In Multispectral Image Processing and Pattern Recognition, edited by Jun Shen, Sharatchandra Pankanti, and Runsheng Wang. SPIE, 2001. http://dx.doi.org/10.1117/12.441637.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Yu, Huan Wu, Jianzhong Zhang, Jingjing Wang, and Xueqiang Zou. "TW-FCM: An Improved Fuzzy-C-Means Algorithm for SPIT Detection." In 2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2018. http://dx.doi.org/10.1109/icccn.2018.8487369.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Santos, Gabriel Marcondes, Emmanuel Tavares Ferreira Affonso, Alisson Marques Silva, and Gray Farias Moita. "Fuzzy C-Means com Método Wrapper Com Baixo Custo Computacional de Seleção de Atributos." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-87.

Full text
Abstract:
Nowadays the Computational Intelligence (IC) algorithms have shown a lot of efficiency in pattern classification and recognition processes. However, some databases may contain irrelevant attributes that may be detrimental to the learning of the classification model. In order to detect and exclude input attributes with little representativeness in the data sets presented to the classification algorithms, the Features Selection (FS) methods are commonly used. The goal of features selection methods is to minimize the number of input attributes processed by a classifier in order to improve its assertiveness. In this way, this work aims to analyze solutions to classification problems with three different classification algorithms. The first approach used for classification is the unsupervised Fuzzy C-Means (FCM) algorithm, the second approach is a supervised version of FCM and the third approach is a variation of supervised FCM with features selection. The method of features selection incorporated in FCM is called the Mean Ratio Feature Selection (MRFS), and was developed with the objective of being a method with low computational cost, without need for complex mathematical equations and can be easily incorporated into any classifier. For the experiments, the three versions of the unsupervised FCM, supervised FCM and FCM with attribute selection were performed with the aim of verifying whether there would be a significant improvement between the variations of the FCM. The results of the experiments showed that FCM with MRFS is promising, with results superior to the original algorithm and also to its supervised version.
APA, Harvard, Vancouver, ISO, and other styles
8

Qu, Fuheng, Yating Hu, Yaohong Xue, and Yong Yang. "A modified possibilistic fuzzy c-means clustering algorithm." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818096.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Bordogna, Gloria, and Gabriella Pasi. "Hierarchical-Hyperspherical Divisive Fuzzy C-Means (H2D-FCM) Clustering for Information Retrieval." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.104.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kang, Jiayin, and Wenjuan Zhang. "Fingerprint Image Segmentation Using Modified Fuzzy C-Means Algorithm." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162858.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Modified fuzzy c-means (FCM)' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography