Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

Provided by: International Scholarly Research Network
Topic: Big Data
Format: PDF
Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which Fuzzy C-Means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved Fuzzy Partitions FCM (GIFP-FCM) is one of them, which uses Lp norm distance measure and competitive learning and outperforms the previous algorithms in this field.

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