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Measuring the congruence of fuzzy partitions in fuzzy c -means clustering

Applied Soft Computing, 2017
Graphical abstractDisplay Omitted HighlightsWe present a new cluster validity index for fuzzy c-means clustering that resorts to information theoretic principles.Overall, it measures the congruence between fuzzy c-partitions and the observed data.We tested its effectiveness using synthetic data as well as real life data.It potentially distinguishes ...
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On Fuzzy $$c$$-Means and Membership Based Clustering

2015
Fuzzy $c$-means is one of the most well known fuzzy clustering algorithms. It is usually solved using an iterative algorithm. This algorithm does not ensure that the solution is the global optimum. In this paper we study the distribution of values of the objective function of fuzzy $c$-means.
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Cluster Forests Based Fuzzy C-Means for Data Clustering

2016
Cluster forests is a novel approach for ensemble clustering based on the aggregation of partial K-means clustering trees. Cluster forests was inspired from random forests algorithm. Cluster forests gives better results than other popular clustering algorithms on most standard benchmarks.
Abdelkarim Ben Ayed, Mohamed Ben Halima, Adel M. Alimi +2 more
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FRCM: A fuzzy rough c-means clustering method