Rough intuitionistic fuzzy C-means algorithm and a comparative analysis
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...[24], developed a novel rough intuitionistic fuzzy k-means algorithm (RIFKM) accommodates graded nonmembership of objects in clusters and the uncertainty through the boundary regions....
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References
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"Rough intuitionistic fuzzy C-means ..." refers methods in this paper
...Several modifications to HCM framework led to the development of various uncertainty based C-Means algorithms such as Rough C-Means (RCM) [7], Fuzzy C-Means (FCM) [2], Rough-Fuzzy C-Means (RFCM) [8, 9, 10, 11] and Intuitionistic Fuzzy CMeans (IFCM) [4]....
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...2 Fuzzy C-Means In 1981 Bezdek developed an extremely powerful method to classify fuzzy data known as fuzzy c-means [2] by using the concept of objective function....
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"Rough intuitionistic fuzzy C-means ..." refers background or methods in this paper
...The Davies-Bouldin (DB) [5] and Dunn (D) indexes [3] are a well-known bench mark for performance analysis of clustering algorithms....
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...But, it was shown by Dubois and Prade [5] that in fact they complement each other and introduced hybrid models of rough fuzzy set and fuzzy sough set which are better models than the individual ones....
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...PERFORMANCE INDEXES The Davis-Bouldin (DB) [5] and Dunn (D) indexes [3] are two of the most basic performance analysis indexes....
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...As mentioned in ([5], abstract) the DB measure does not depend on neither the number of clusters analysed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm....
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