Aggregation of Standard and Entropy Based Fuzzy c-Means Clustering by a Modified Objective Function

Ichihashi, Hidetomo; Honda, Katsuhiro; Notsu, Akira; Hattori, Takumu

doi:10.1109/foci.2007.371510

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Quadratic terms or entropy terms were added to the objective function of the basic FCM to cater to specic needs. Ichihashi et al (2007) proposed a generalized FCM clustering method, which generalized the basic objective function of FCM, so that by changing the coefficients of objective functions either quadratic function-based FCM or entropy term-based FCM could be attained.…”

Section: Literature Surveymentioning

confidence: 99%

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Dey

Pratihar

Datta

2011

Fuzzy Optim Decis Making

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A modified approach had been developed in this study by combining two well-known algorithms of clustering, namely fuzzy c-means algorithm and entropybased algorithm. Fuzzy c-means algorithm is one of the most popular algorithms for fuzzy clustering. It could yield compact clusters but might not be able to generate distinct clusters. On the other hand, entropy-based algorithm could obtain distinct clusters, which might not be compact. However, the clusters need to be both distinct as well as compact. The present paper proposes a modified approach of clustering by combining the above two algorithms. A genetic algorithm was utilized for tuning of all three clustering algorithms separately. The proposed approach was found to yield both distinct as well as compact clusters on two data sets.

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Section: Literature Surveymentioning

confidence: 99%

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Dey

Pratihar

Datta

2011

Fuzzy Optim Decis Making

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“…Successively, it removes all data points having dissimilarity larger than a threshold with the chosen cluster center; the procedure is repeated until all data points are removed. Ichihashi (2000) and Miyagishi et al (2000) suggested a generalized objective function with additional variables. These authors consider a covariance matrix and show an equivalence between their Kullback-Leibler (KL) fuzzy clustering and the Gaussian mixture model.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

et al. 2023

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In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation.

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“…In the FCMC, the standard FCM clustering objective function [1] is slightly generalized and the iteratively reweighted least square (IRLS) technique [10] is applied [11]. Cluster memberships are defined by a function of Mahalanobis distances between data vectors and cluster centers.…”

Section: Introductionmentioning

confidence: 99%

FCM classifier for high-dimensional data

Ichihashi

Honda

Notsu

et al. 2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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Abstract-A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional data. This paper proposes a way of directly handling high-dimensional data in the FCM clustering and classification. The proposed classifier without any preprocessing outperforms the k-nearest neighbor (k-NN) classifier with PCA on the benchmark set of COREL image collection.

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Aggregation of Standard and Entropy Based Fuzzy c-Means Clustering by a Modified Objective Function

Cited by 8 publications

References 17 publications

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

FCM classifier for high-dimensional data

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