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INTRODUCING TO FIVE DATA CLUSTERING ALGORITHMS
Clustering is one of the primary tasks of data mining. Over the years, many methods have been developed for clustering patterns. Each method can have its own technique (i.e. partitioning or hierarchical), mode (on-line or off-line), approach (fuzzy or crisp clustering), or special purpose (i.e. for sequential data set, very large database, etc.). This paper aims to introduce the most representative algorithms used in off-line mode that apply crisp or fuzzy approach. The algorithms are K-Means, Fuzzy C-Means (FCM), Mountain, Subtractive and psFCM. The implementation of two of the algorithms using Matlab is provided in the appendix.
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