Journal articles on the topic 'Fuzzy clusters'
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1
Martino, Ferdinando Di, and Salvatore Sessa. "A New Validity Index Based on Fuzzy Energy and Fuzzy Entropy Measures in Fuzzy Clustering Problems." Entropy 22, no. 11 (October 23, 2020): 1200. http://dx.doi.org/10.3390/e22111200.
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Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI (University of California at Irvine) machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of fuzzy C-means by using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.
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Ryoo, Ji Hoon, Seohee Park, Seongeun Kim, and Hyun Suk Ryoo. "Efficiency of Cluster Validity Indexes in Fuzzy Clusterwise Generalized Structured Component Analysis." Symmetry 12, no. 9 (September 14, 2020): 1514. http://dx.doi.org/10.3390/sym12091514.
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Fuzzy clustering has been broadly applied to classify data into K clusters by assigning membership probabilities of each data point close to K centroids. Such a function has been applied into characterizing the clusters associated with a statistical model such as structural equation modeling. The characteristics identified by the statistical model further define the clusters as heterogeneous groups selected from a population. Recently, such statistical model has been formulated as fuzzy clusterwise generalized structured component analysis (fuzzy clusterwise GSCA). The same as in fuzzy clustering, the clusters are enumerated to infer the population and its parameters within the fuzzy clusterwise GSCA. However, the identification of clusters in fuzzy clustering is a difficult task because of the data-dependence of classification indexes, which is known as a cluster validity problem. We examined the cluster validity problem within the fuzzy clusterwise GSCA framework and proposed a new criterion for selecting the most optimal number of clusters using both fit indexes of the GSCA and the fuzzy validity indexes in fuzzy clustering. The criterion, named the FIT-FHV method combining a fit index, FIT, from GSCA and a cluster validation measure, FHV, from fuzzy clustering, performed better than any other indices used in fuzzy clusterwise GSCA.
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GREKOUSIS, GEORGE. "GIVING FUZZINESS TO SPATIAL CLUSTERS: A NEW INDEX FOR CHOOSING THE OPTIMAL NUMBER OF CLUSTERS." International Journal on Artificial Intelligence Tools 22, no. 03 (June 2013): 1350009. http://dx.doi.org/10.1142/s0218213013500097.
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Choosing the optimal number of clusters is a key issue in cluster analysis. Especially when dealing with more spatial clustering, things tend to be more complicated. Cluster validation helps to determine the appropriate number of clusters present in a dataset. Furthermore, cluster validation evaluates and assesses the results of clustering algorithms. There are numerous methods and techniques for choosing the optimal number of clusters via crisp and fuzzy clustering. In this paper, we introduce a new index for fuzzy clustering to determine the optimal number of clusters. This index is not another metric for calculating compactness or separation among partitions. Instead, the index uses several existing indices to give a degree, or fuzziness, to the optimal number of clusters. In this way, not only do the objects in a fuzzy cluster get a membership value, but the number of clusters to be partitioned is given a value as well. The new index is used in the fuzzy c-means algorithm for the geodemographic segmentation of 285 postal codes.
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Azad, Puneet, and Vidushi Sharma. "Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment." ISRN Sensor Networks 2013 (February 24, 2013): 1–8. http://dx.doi.org/10.1155/2013/909086.
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Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster’s nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster heads. In this paper, we present a fuzzy decision-making approach for the selection of cluster heads. Fuzzy multiple attribute decision-making (MADM) approach is used to select CHs using three criteria including residual energy, number of neighbors, and the distance from the base station of the nodes. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol in homogeneous environments.
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BORGELT, CHRISTIAN. "RESAMPLING FOR FUZZY CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 05 (October 2007): 595–614. http://dx.doi.org/10.1142/s0218488507004893.
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Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper I give an overview how such resampling approaches can be transferred to fuzzy and probabilistic clustering. I study several cluster comparison measures, which can be parameterized with t-norms, and report experiments that provide some guidance which of them may be the best choice.
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Borisov, Vadim, Maksim Dli, Artem Vasiliev, Yaroslav Fedulov, Elena Kirillova, and Nikolay Kulyasov. "Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering." Energies 14, no. 18 (September 15, 2021): 5848. http://dx.doi.org/10.3390/en14185848.
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A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.
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Rajkumar, K. Varada, Adimulam Yesubabu, and K. Subrahmanyam. "Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2760. http://dx.doi.org/10.11591/ijece.v9i4.pp2760-2770.
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A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
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Chen, Min, and Simone A. Ludwig. "Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters." Journal of Artificial Intelligence and Soft Computing Research 4, no. 1 (January 1, 2014): 43–56. http://dx.doi.org/10.2478/jaiscr-2014-0024.
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Abstract Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used
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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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Honda, Katsuhiro, and Hidetomo Ichihashi. "A Regularization Approach to Fuzzy Clustering with Nonlinear Membership Weights." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 1 (January 20, 2007): 28–34. http://dx.doi.org/10.20965/jaciii.2007.p0028.
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Fuzzyc-means (FCM) is the fuzzy version ofc-means clustering, in which memberships are fuzzified by introducing an additional parameter into the linear objective function of the weighted sum of distances between datapoints and cluster centers. Regularization of hardc-means clustering is another approach to fuzzification, in which regularization terms such as entropy and quadratic terms have been adopted. We generalized the fuzzification concept and propose a new approach to fuzzy clustering in which linear weights of hardc-means clustering are replaced by nonlinear ones through regularization. Numerical experiments demonstrated that the proposed algorithm has the characteristic features of the standard FCM algorithm and of regularization approaches. One of the proposed nonlinear weights makes it possible to both to attract data to clusters and to repulse different clusters. This feature derives different types of fuzzy classification functions in both probabilistic and possibilistic models.
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Honda, Katsuhiro, Nami Yamamoto, Seiki Ubukata, and Akira Notsu. "Noise Rejection in MMMs-Induced Fuzzy Co-Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 7 (November 20, 2017): 1144–51. http://dx.doi.org/10.20965/jaciii.2017.p1144.
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Noise rejection is an important issue in practical application of FCM-type fuzzy clustering, and noise clustering achieves robust estimation of cluster prototypes with an additional noise cluster for dumping noise objects into it. Noise objects having larger distances from all clusters are designed to be assigned to the noise cluster, which is located in an equal (fixed) distance from all objects. Fuzzy co-clustering is an extended version of FCM-type clustering for handling cooccurrence information among objects and items, where the goal of analysis is to extract pair-wise clusters of familiar objects and items. This paper proposes a novel noise rejection model for fuzzy co-clustering induced by multinomial mixture models (MMMs), where a noise cluster is defined with homogeneous item memberships for drawing noise objects having dissimilar cooccurrence features from all general clusters. The noise rejection scheme can be also utilized in selecting the optimal cluster number through a sequential implementation with different cluster numbers.
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Vidojević, Filip, Dušan Džamić, and Miroslav Marić. "E-function for Fuzzy Clustering in Complex Networks." Ipsi Transactions on Internet research 18, no. 1 (January 1, 2022): 17–21. http://dx.doi.org/10.58245/ipsi.tir.22jr.04.
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In many real-life situations, data consists of entities and the connections between them, which are naturally described by a complex network (graph). The structure of the network is often such that it is possible to group nodes based on the existence of connections between them, where such groups are called clusters (communities, modules). If the nodes are allowed to partially belong to clusters, they are called fuzzy (overlapping) clusters. There is a huge number of algorithms in the literature that perform fuzzy clustering, that is finds overlapping clusters, so a mechanism is needed to evaluate such clustering. The function that assesses the quality of a performed clustering is called the cluster quality function. One of the latest proposed quality functions is the E-function. The E-function is based on a comparison of the internal structure of a cluster, i.e., the connection between nodes within a cluster and the connection of its nodes with the nodes of other clusters. Due to its exponential nature, the E-function is sensitive to small changes in the membership degrees to which the nodes belong to clusters. As such, it has shown good results in evaluating clustering on known data sets. In this paper, the experimental results that the modified E-function achieves in the case of overlapping clusters are presented. Also, some possibilities for fuzzy clustering by optimizing the E-function are displayed.
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Ren, Min, Peiyu Liu, Zhihao Wang, and Jing Yi. "A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2647389.
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For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.
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Julie, E. Golden, and S. Tamil Selvi. "Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach." Scientific World Journal 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5063261.
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Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.
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N, Bhalaji. "Cluster Formation using Fuzzy Logic in Wireless Sensor Networks." March 2021 3, no. 1 (March 9, 2021): 31–39. http://dx.doi.org/10.36548/jsws.2021.1.004.
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The biggest challenges faced by wireless sensor networks (WSNs) are the network lifetime and consumption of energy. To reduce the amount of energy used by WSNs, high quality clustering proves to be a crucial approach. There are multiple criteria that need to be evaluated depending on the cluster’s quality and incorporating all these criteria will prove to be cumbersome process, leading to high-quality clustering. Hence, in this paper we propose an algorithm that is used to produce high quality clusters. Cluster quality is set as the deciding criterion to determine the quality of the clusters thereby categorizing them as intra- and inter-clusters based on their distances to eliminate error rate. Using fuzzy logic, the optimal cluster head is chosen. Similarly, based on the maximum and minimum distance between the nodes, the maximum and minimum energy present in every cluster is determined. The major advantages of the proposed methodology are large-scale networks with large nodes count, better scalability, independence of key CHs, low error rate and high reliability. Using internal and external criteria, the validity of the clustering quality can be measured. Experimental simulation shows that the proposed methodology will be useful in improving the network lifetime and energy consumption. Hence the proposed node further enhances the death of the last node and first node when compared using other methodology.
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Shokrollahi, Ayub, and Babak Mazloom-Nezhad Maybodi. "An Energy-Efficient Clustering Algorithm Using Fuzzy C-Means and Genetic Fuzzy System for Wireless Sensor Network." Journal of Circuits, Systems and Computers 26, no. 01 (October 4, 2016): 1750004. http://dx.doi.org/10.1142/s0218126617500049.
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The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.
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Asrriningtias, Salnan Ratih. "Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior." Journal of Development Research 5, no. 1 (May 31, 2021): 7–12. http://dx.doi.org/10.28926/jdr.v5i1.134.
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One of the strategies in order to compete in Batik MSMEs is to look at the characteristics of the customer. To make it easier to see the characteristics of customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index, NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value
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Ubukata, Seiki, Katsuya Koike, Akira Notsu, and Katsuhiro Honda. "MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 747–58. http://dx.doi.org/10.20965/jaciii.2018.p0747.
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In the field of cluster analysis, fuzzy theory including the concept of fuzzy sets has been actively utilized to realize flexible and robust clustering methods. FuzzyC-means (FCM), which is the most representative fuzzy clustering method, has been extended to achieve more robust clustering. For example, noise FCM (NFCM) performs noise rejection by introducing a noise cluster that absorbs noise objects and possibilisticC-means (PCM) performs the independent extraction of possibilistic clusters by introducing cluster-wise noise clusters. Similarly, in the field of co-clustering, fuzzy co-clustering induced by multinomial mixture models (FCCMM) was proposed and extended to noise FCCMM (NFCCMM) in an analogous fashion to the NFCM. Ubukata et al. have proposed noise clustering-based possibilistic co-clustering induced by multinomial mixture models (NPCCMM) in an analogous fashion to the PCM. In this study, we develop an NPCCMM scheme considering variable cluster volumes and the fuzziness degree of item memberships to investigate the specific aspects of fuzzy nature rather than probabilistic nature in co-clustering tasks. We investigated the characteristics of the proposed NPCCMM by applying it to an artificial data set and conducted document clustering experiments using real-life data sets. As a result, we found that the proposed method can derive more flexible possibilistic partitions than the probabilistic model by adjusting the fuzziness degrees of object and item memberships. The document clustering experiments also indicated the effectiveness of tuning the fuzziness degree of object and item memberships, and the optimization of cluster volumes to improve classification performance.
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Kılıçman, Adem, and Jaisree Sivalingam. "Portfolio Optimization of Equity Mutual Funds—Malaysian Case Study." Advances in Fuzzy Systems 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/879453.
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We focus on the equity mutual funds offered by three Malaysian banks, namely Public Bank Berhad, CIMB, and Malayan Banking Berhad. The equity mutual funds or equity trust is grouped into four clusters based on their characteristics and categorized as inferior, stable, good performing, and aggressive funds based on their return rates, variance and treynor index. Based on the cluster analysis, the return rates and variance of clusters are represented as triangular fuzzy numbers in order to reflect the uncertainty of financial market. To find the optimal asset allocation in each cluster we develop a hybrid model of optimization and fuzzy based on return rates, variance. This was done by maximizing the fuzzy return for a tolerable fuzzy risk and minimizing the fuzzy risk for a desirable fuzzy return separately at different confidence levels.
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YANG, MIIN-SHEN, CHIH-YING LIN, and YI-CHENG TIAN. "A ROBUST FUZZY CLASSIFICATION MAXIMUM LIKELIHOOD CLUSTERING FRAMEWORK." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 05 (October 2013): 755–76. http://dx.doi.org/10.1142/s0218488513500360.
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In 1993, Yang first extended the classification maximum likelihood (CML) to a so-called fuzzy CML, by combining fuzzy c-partitions with the CML function. Fuzzy c-partitions are generally an extension of hard c-partitions. It was claimed that this was more robust. However, the fuzzy CML still lacks some robustness as a clustering algorithm, such as its in-ability to detect different volumes of clusters, its heavy dependence on parameter initializations and the necessity to provide an a priori cluster number. In this paper, we construct a robust fuzzy CML clustering framework that has a robust clustering method. The eigenvalue decomposition of a covariance matrix is firstly considered using the fuzzy CML model. The Bayesian information criterion (BIC) is then used for model selection, in order to choose the best model with the optimal number of clusters. Therefore, the proposed robust fuzzy CML clustering framework exhibits clustering characteristics that are robust in terms of the parameter initialization, robust in terms of the cluster number and also in terms of its capability to detect different volumes of clusters. Numerical examples and real data applications with comparisons are provided, which demonstrate the effectiveness and superiority of the proposed method.
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Honda, Katsuhiro, Yoshiki Hakui, Seiki Ubukata, and Akira Notsu. "A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (January 20, 2020): 40–47. http://dx.doi.org/10.20965/jaciii.2020.p0040.
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MMMs-induced fuzzy co-clustering achieves dual partition of objects and items by estimating two different types of fuzzy memberships. Because memberships of objects and items are usually estimated under different constraints, the conventional models mainly targeted object clusters only, but item memberships were designed for representing intra-cluster typicalities of items, which are independently estimated in each cluster. In order to improve the interpretability of co-clusters, meaningful items should not belong to multiple clusters such that each co-cluster is characterized by different representative items. In previous studies, the item sharing penalty approach has been applied to the MMMs-induced model but the dual exclusive constraints approach has not yet. In this paper, a heuristic-based approach in FCM-type co-clustering is modified for adopting in MMMs-induced fuzzy co-clustering and its characteristics are demonstrated through several comparative experiments.
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Jebari, Khalid, Abdelaziz Elmoujahid, and Aziz Ettouhami. "Automatic Genetic Fuzzy c-Means." Journal of Intelligent Systems 29, no. 1 (April 25, 2018): 529–39. http://dx.doi.org/10.1515/jisys-2018-0063.
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Abstract Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a new mutation operator, and modified tournament selection; further, it defines a new fitness function based on three cluster validity indices. Real data sets are used to demonstrate the effectiveness, in terms of quality, of the proposed algorithm.
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Urumov, Georgy, and Panagiotis Chountas. "Clustering stock price volatility using intuitionistic fuzzy sets." Notes on Intuitionistic Fuzzy Sets 28, no. 3 (September 8, 2022): 343–52. http://dx.doi.org/10.7546/nifs.2022.28.3.343-352.
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Clustering involves gathering a collection of objects into homogeneous groups or clusters, such that objects in the same cluster are more similar when compared to objects present in other groups. Clustering algorithms that generate a tree of clusters called dendrogram which can be either divisive or agglomerative. The partitional clustering gives a single partition of objects, with a predefined K number of clusters. The most popular partition clustering approaches are: k-means and fuzzy C-means (FCM). In k-means clustering, data are divided into a number of clusters where data elements belong to exactly one cluster. The k-means clustering works well when data elements are well separable. To overcome the problem of non-separability, FCM and IFCM clustering algorithm were proposed. Here we review the use of FCM/IFCM with reference to the problem of market volatility.
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Endo, Yasunori, Ayako Heki, and Yukihiro Hamasuna. "Non Metric Model Based on Rough Set Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 540–51. http://dx.doi.org/10.20965/jaciii.2013.p0540.
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The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. The fuzzy degree of membership may, however, be too descriptive for interpreting clustering results. Rough set representation handles such cases. Clustering based on rough sets could provide a solution that is less restrictive than conventional clustering and more descriptive than fuzzy clustering. This paper covers two types of Rough-set-based Non Metric model (RNM). One algorithm is the Roughset-based Hard Non Metric model (RHNM) and the other is the Rough-set-based Fuzzy Non Metric model (RFNM). In both algorithms, clusters are represented by rough sets and each cluster consists of lower and upper approximation. The effectiveness of proposed algorithms is evaluated through numerical examples.
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Feng, Xue Bo, Fang Yao, Zhi Gang Li, and Xiao Jing Yang. "Improved Fuzzy C-Means Based on the Optimal Number of Clusters." Applied Mechanics and Materials 392 (September 2013): 803–7. http://dx.doi.org/10.4028/www.scientific.net/amm.392.803.
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According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The improved FCM has good clustering effect, enhances the optimized capability, and improves the efficiency and effectiveness of the clustering. It has better tightness in the class, scatter among classes and cluster stability and faster convergence rate than the traditional FCM clustering method.
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Hao, ZiQi, ZhenJiang Zhang, and Han-Chieh Chao. "A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/641235.
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As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We usek-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.
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Rahmalia, Dinita. "Optimizing the Membership Degree of Fuzzy Inference System (FIS) and Fuzzy Clustering Means (FCM) in Weather Data Using Firefly Algorithm." CAUCHY 6, no. 4 (May 30, 2021): 169–80. http://dx.doi.org/10.18860/ca.v6i4.8933.
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In weather clustering, there are many variables which can be observed such as air temperature, humidity, sunlight intensity, and so on. In this research, Takagi-Sugeno Fuzzy Inference System (FIS) will be used for forecasting the sunlight intensity based on temperature and humidity and Fuzzy Clustering Means (FCM) will be used for clustering them based on fuzzy set. From the data consisting of temperature, humidity, and sunlight intensity, we will forecast sunlight intensity and cluster them into two clusters, three clusters, and four clusters by FCM method. In FIS method, the membership degree are often generated by trial and error. Also, the optimization of the initial of membership degree are required in FCM. Because the initial of membership degree are often generated by trial and error, in this research, we use heuristic method like Firefly Algorithm to optimize the membership degree. From the simulations, Firefly Algorithm can optimize the membership degree of FIS for forecasting the data with minimum Mean Square Error (MSE) and the initial of membership degree of FCM with two clusters, three clusters, and four clusters with minimum objective value.
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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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GUO, GONGDE, and DANIEL NEAGU. "FUZZY kNNMODEL APPLIED TO PREDICTIVE TOXICOLOGY DATA MINING." International Journal of Computational Intelligence and Applications 05, no. 03 (September 2005): 321–33. http://dx.doi.org/10.1142/s1469026805001635.
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A robust method, fuzzy kNNModel, for toxicity prediction of chemical compounds is proposed. The method is based on a supervised clustering method, called kNNModel, which employs fuzzy partitioning instead of crisp partitioning to group clusters. The merits of fuzzy kNNModel are two-fold: (1) it overcomes the problems of choosing the parameter ε — allowed error rate in a cluster and the parameter N — minimal number of instances covered by a cluster, for each data set; (2) it better captures the characteristics of boundary data by assigning them with different degrees of membership between 0 and 1 to different clusters. The experimental results of fuzzy kNNModel conducted on thirteen public data sets from UCI machine learning repository and seven toxicity data sets from real-world applications, are compared with the results of fuzzy c-means clustering, k-means clustering, kNN, fuzzy kNN, and kNNModel in terms of classification performance. This application shows that fuzzy kNNModel is a promising method for the toxicity prediction of chemical compounds.
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Yang, Li, and Zhijian Hu. "Implementation of Dynamic Virtual Inertia Control of Supercapacitors for Multi-Area PV-Based Microgrid Clusters." Sustainability 12, no. 8 (April 18, 2020): 3299. http://dx.doi.org/10.3390/su12083299.
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In order to improve the dynamic stability of multi-area microgrid (MG) clusters in the autonomous mode, this study proposes a novel fuzzy-based dynamic inertia control strategy for supercapacitors in multi-area autonomous MG clusters. By virtue of the integral manifold theory, the interactive influence of inertia on dynamic stability for multi-area MG clusters is explored in detail. The energy function of multi-area MG clusters is constructed to further analyze the inertia constant. Based on the analysis of the mechanism, a control strategy for the fuzzy-based dynamic inertia control of supercapacitors for multi-area MG clusters is further proposed. For each sub-microgrid (sub-MG), the gain of the fuzzy-based dynamic inertia control is self-tuned dynamically, with system events being triggered, so as to flexibly and robustly enhance the dynamic performance of the multi-area MG clusters in the autonomous mode. To verify the effectiveness of the proposed control scheme, a three-area photovoltaic (PV)-based MG cluster is designed and simulated on the MATLAB/Simulink platform. Moreover, a comparison between the dynamic fuzzy-based inertial control method and an additional droop control method is finally presented to validate the advantages of the fuzzy-based dynamic inertial control approach.
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Kurniasari, Ratna, Rukun Santoso, and Alan Prahutama. "ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING." Jurnal Gaussian 10, no. 4 (December 31, 2021): 544–53. http://dx.doi.org/10.14710/j.gauss.v10i4.33101.
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Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members.
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Xia, Hong, Qingyi Dong, Hui Gao, Yanping Chen, and ZhongMin Wang. "Service Partition Method Based on Particle Swarm Fuzzy Clustering." Wireless Communications and Mobile Computing 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/7225552.
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It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.
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Liparulo, Luca, Andrea Proietti, and Massimo Panella. "Fuzzy Clustering Using the Convex Hull as Geometrical Model." Advances in Fuzzy Systems 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/265135.
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A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.
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Chen, Wei Jin, Huai Lin Dong, Qing Feng Wu, and Ling Lin. "Research on Fuzzy Clustering Validity." Applied Mechanics and Materials 40-41 (November 2010): 174–82. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.174.
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The evaluation of clustering validity is important for clustering analysis, and is one of the hottest spots of cluster analysis. The quality of the evaluation of clustering is that optimal number of clusters is reasonable. For fuzzy clustering, the paper surveys the widely known fuzzy clustering validity evaluation based on the methods of fuzzy partition, geometry structure and statistics.
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Zhang, Dawei, Fuding Xie, Dapeng Wang, Yong Zhang, and Yan Sun. "Cluster Analysis Based on Bipartite Network." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/676427.
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Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzyc-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster numbercin advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method.
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Viattchenin, Dmitri A. "Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters." Foundations of Computing and Decision Sciences 41, no. 1 (March 1, 2016): 45–76. http://dx.doi.org/10.1515/fcds-2016-0003.
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AbstractThe paper deals with the problem of discovering fuzzy clusters with optimal number of elements in heuristic possibilistic clustering. The relational clustering procedure using a parameter that controls cluster sizes is considered and a technique for detecting the optimal number of elements in fuzzy clusters is proposed. The effectiveness of the proposed technique is illustrated through numerical examples. Experimental results are discussed and some preliminary conclusions are formulated.
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Khanali, Hoda, and Babak Vaziri. "Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data." Statistics, Optimization & Information Computing 9, no. 3 (July 10, 2021): 618–29. http://dx.doi.org/10.19139/soic-2310-5070-1035.
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Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM). So it reduces the sensitivity to noisy and outlier data, and enhances performance and quality of clusters. Since FVCM allocates some data to a specific cluster based on similarity technique, reducing the effect of noisy data increases the quality of the clusters. This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that at each stage samples with a high degree of membership are selected for training in the classification of FSVM. Then, the labels of the remaining samples are predicted so the process continues until the convergence of the FVCM-FSVM. The results of the numerical experiments showed the proposed approach has better performance than FVCM. Of course, it greatly achieves high accuracy.
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KINANTI, Kartika Ayu, Hari SUKARNO, and Elok Sri UTAMI. "Banking Clustering Study Based On Fuzzy C-mean and Fuzzy Gustafson Kessel." International Journal of Environmental, Sustainability, and Social Science 2, no. 1 (April 1, 2021): 1–6. http://dx.doi.org/10.38142/ijesss.v2i1.58.
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The banking sector as one of the economic drivers plays an important role in society. Over time, bank operations did not only raise funds from the public but were more complex. The development of the banking industry can be seen from the number of banks in Indonesia that have spurred the level of competition. Of course, the bank must pay attention to its health. The use of bank soundness level parameters or RGEC combined with clusters is interesting to study. By using the cluster method, banks can be classified based on the parameters of their health level. This study aims to analyze the RGEC-based bank grouping classification generated by the Fuzzy C-Means and Fuzzy Gustafson Kessel clustering analysis using financial ratio data on 80 conventional banks in Indonesia. The software used in this study is Matlab r2015b. The results showed that the FCM clustering had a smaller standard deviation than FGK so that the first cluster in the FCM showed that the banks were in good condition compared to the other clusters even though the overall condition of banks in Indonesia was good when viewed from their financial performance.
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Relangi, Naga Durga Satya Siva Kiran, Aparna Chaparala, and Radhika Sajja. "Identification of Potential Quality of Groundwater Using Improved Fuzzy C Means Clustering Method." Mathematical Modelling of Engineering Problems 9, no. 5 (December 13, 2022): 1369–77. http://dx.doi.org/10.18280/mmep.090527.
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The groundwater quality assessment gained more attention among the water quality management stations and researchers. The conventional water quality index method and artificial neural network models are used to assess groundwater. But these models are inadequate to handle data with uncertainty. In this work, we propose an improved Fuzzy C Means clustering method to identify the homogeneous clusters with respect to groundwater quality. For this purpose 1020 groundwater samples data with 7 physiochemical parameters of the year 2019 are collected from West Godavari, Andhra Pradesh, India. The effectiveness of the proposed clustering method is evaluated with two standard clustering methods namely K-means and Fuzzy C Means. The initial selection of the number of clusters and cluster centers determines the success of both the conventional K Means and Fuzzy C Means clustering methods. The proposed improved Fuzzy C Means method identifies the optimal number of clusters based on the water index value. The proposed improved Fuzzy C Means clustering method is implemented on the groundwater data set. The performance is computed with the help of the silhouette score and Davies Bouldin Index. The proposed clustering method outperforms with the existing K Means and Fuzzy C Means with silhouette score of 0.857 and Davies Bouldin Index value of 0.502 when the number of clusters are 4.
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Chaudhuri, Arindam. "Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms." Advances in Fuzzy Systems 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/238237.
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Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.
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Chen, Chin Chun, Yuan Horng Lin, Jeng Ming Yih, and Sue Fen Huang. "Construct Knowledge Structure of Linear Algebra." Advanced Materials Research 211-212 (February 2011): 793–97. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.793.
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Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.
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Fang, Changjian, Dejun Mu, Zhenghong Deng, Jun Hu, and Chen-He Yi. "Fast detection of the fuzzy communities based on leader-driven algorithm." International Journal of Modern Physics B 32, no. 06 (February 26, 2018): 1850058. http://dx.doi.org/10.1142/s0217979218500583.
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In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.
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Pimentel, Bruno Almeida, and Renata M. C. R. de Souza. "A Generalized Multivariate Approach for Possibilistic Fuzzy C-Means Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (November 27, 2018): 893–916. http://dx.doi.org/10.1142/s021848851850040x.
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Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitivity defect of FCM and overcomes the coincident clusters problem of PCM. Although PFCM have shown good performance in cluster detection, it does not consider that different variables can produce different membership and possibility degrees and this can improve the clustering quality as it has been performed with the Multivariate Fuzzy c-Means (MFCM). Here, this work presents a generalized multivariate approach for possibilistic fuzzy c-means clustering. This approach gives a general form for the clustering criterion of the possibilistic fuzzy clustering with membership and possibility degrees different by cluster and variable and a weighted squared Euclidean distance in order to take into account the shape of clusters. Six multivariate clustering models (special cases) can be derivative from this general form and their properties are presented. Experiments with real and synthetic data sets validate the usefulness of the approach introduced in this paper using the special cases.
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Putri, Ghina Nabila Saputro, Dwi Ispriyanti, and Tatik Widiharih. "IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA." Jurnal Gaussian 11, no. 1 (May 13, 2022): 86–98. http://dx.doi.org/10.14710/j.gauss.v11i1.33996.
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Social media has become the most popular media, which can be accessed by young to old age. Twitter became one of the effective media and the familiar one used by the public, thus making the company make Twitter one of the promotional tools, one of which is Tokopedia. The research aims to group tweets uploaded by @tokopedia Twitter accounts based on the type of tweets content that gets a lot of retweets and likes by followers of @tokopedia. Application of text mining to cluster tweets on the @tokopedia Twitter account using Fuzzy C-Means and Fuzzy Possibilistic C-Means algorithms that viewed the accuracy comparison of both methods used the Modified Partition Coefficient (MPC) cluster validity. The clustering process was carried out five times by the number of clusters ranging from 3 to 7 clusters. The results of the study showed the Fuzzy C-Means method is a better method compared to the Fuzzy Possibilistic C-Means method in clustering data tweets, with the number of clusters formed is 4. The content type formed is related to promo, discount, cashback, prize quizzes, and event promotions organized by Tokopedia. Content with the highest average number of retweets and likes is about automotive deals, sports tools, and merchandise offerings. So, that PT Tokopedia can use this content type as a tool for advertising on Twitter because it gets more likes by followers of @tokopedia.Keywords: Data Tweets, Clustering, Fuzzy C-Means, Fuzzy Possibilistics C-Means, Modified Partition Coefficient.
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Nguyen, Thuan Tan, Ban Van Doan, Chau Ngoc Truong, and Trinh Thi Thuy Tran. "Clustering and Query Optimization in Fuzzy Object-Oriented Database." International Journal of Natural Computing Research 8, no. 1 (January 2019): 1–17. http://dx.doi.org/10.4018/ijncr.2019010101.
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The purpose of the clustering method is to provide some meaningful partitioning of the data set. In general, finding separate clusters with similar members is essential. A problem in clustering is how to determine the number of optimal clusters that best fits the data set. Most clustering algorithms generate a partition based on input parameters (for example, cluster number, minimum density) which results in limiting the number of clusters. Therefore, the article proposes an improved EMC clustering algorithm that is more flexible in handling and manipulating those clusters, where input parameter values are assumed to be different clusters for different partitions of a data set. In addition, based on the above partitioning results, this article proposes a new approach to processing and optimizing fuzzy queries to improve efficiency in the manipulation and processing of specific data such as (less time consuming, less resource consuming)
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Inoue, Kohei, and Kiichi Urahama. "Robust Fuzzy Clustering Based on Similarity between Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (March 20, 2004): 115–20. http://dx.doi.org/10.20965/jaciii.2004.p0115.
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We present a robust fuzzy clustering method that utilizes a sequential cluster extraction scheme. In contrast to heuristic sequential methods, our algorithm is derived from an optimization problem and is an iterative solution to it. Our method is non-parametric and includes no heuristic parameter, and can deal with asymmetric similarity data. The determination of the number of clusters is simple and is based on a monotonic property of extracted cluster volumes. Our method can extract arbitrarily shaped clusters by extending the measure of distance between data to a shortest path length. The performance of the method is demonstrated for clustering of an image database and the segmentation of images.
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Yasuda, Makoto, Takeshi Furuhashi, and Shigeru Okuma. "Phase Transitions in Fuzzy Clustering Based on Fuzzy Entropy." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 3 (October 20, 2003): 370–76. http://dx.doi.org/10.20965/jaciii.2003.p0370.
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We studied the statistical mechanical characteristics of fuzzy clustering regularized with fuzzy entropy. We obtained Fermi-Dirac distribution as a membership function by regularizing the fuzzy c-means with fuzzy entropy. We then formulated it as direct annealing clustering, and determined the meanings of the Fermi-Dirac function and fuzzy entropy from the statistical mechanical point of view, and showed that this fuzzy clustering is a part of Fermi-Dirac statistics. We also derived the critical temperature at which phase transition occurs in this fuzzy clustering. Then, with a combination of cluster divisions by phase transitions and an adequate division termination condition, we derived fuzzy clustering that automatically determined the number of clusters, as verified by numerical experiments.
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Sinh, Mai Dinh, Le Hung Trinh, and Ngo Thanh Long. "COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR MULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION." Vietnam Journal of Science and Technology 54, no. 3 (June 16, 2016): 300. http://dx.doi.org/10.15625/0866-708x/54/3/6463.
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This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to classify on multispectral satellite images by relying on fuzzy probability to calculate the number of clusters and the centroid of clusters then using fuzzy clustering to classifying land-cover on the satellite image. In fact, the classification algorithms, the initialization of the clusters and the initial centroid of clusters have great influence on the stability of the algorithms, dealing time and classification results; the unsupervised classification algorithms such as k-Means, c-Means, Iso-data are used quite common for many problems, but the disadvantages is the low accuracy and unstable, especially when dealing with the problems on the satellite image. Results of the algorithm which are proposed show significant reduction of noise in the clusters and comparison with various clustering algorithms like k-means, iso-data, so on.
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Zhong, Yanfei, and Liangpei Zhang. "A New Fuzzy Clustering Algorithm Based on Clonal Selection for Land Cover Classification." Mathematical Problems in Engineering 2011 (2011): 1–21. http://dx.doi.org/10.1155/2011/708459.
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A new fuzzy clustering algorithm based on clonal selection theory from artificial immune systems (AIS), namely, FCSA, is proposed to obtain the optimal clustering result of land cover classification withouta prioriassumptions on the number of clusters. FCSA can adaptively find the optimal number of clusters and is designed as a two-layer system: the classification layer and the optimization layer. The classification layer of FCSA, inspired by clonal selection theory, generates the optimal classification result with a fixed cluster number by utilizing the clone, mutation, and selection of immune operators. The optimization layer of FCSA evaluates the optimal solutions according to performance measures for cluster validity and then adjusts the cluster number to output the final optimal cluster number. Two experiments with different types of image evince that FCSA not only finds the optimal number of clusters, but also consistently outperforms the traditional clustering algorithms, such as K-means and Fuzzy C-means. Hence, FCSA provides an effective option for performing the task of land cover classification.
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Badanina, Natalya Dmitriyevna, Aleksandra Andreevna Zinchenko, and Vladimir Anatolievich Sudakov. "Ranking of objects based on fuzzy clustering." Keldysh Institute Preprints, no. 68 (2022): 1–12. http://dx.doi.org/10.20948/prepr-2022-68.
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The paper creates a method for using fuzzy logic in the task of dividing objects into clusters and ranking them, where their coordinates are indicated as features. Classical machine learning approaches for making clustering models are investigated. Further, they are supplemented with the fuzzy numbers apparatus to obtain an estimate of the potential possibility of an object belonging to a cluster. Based on the selected approaches, algorithmic and software were developed for assigning an object to clusters with the derivation of the membership function, as well as the derivation of the rank calculated through defuzzification, taking into account the importance of each cluster. The resulting model can be used to solve the problems of selecting and ranking objects, taking into account the degree of confidence in their belonging to certain classes.