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Comparative Performance Of Using PCA With K-Means And Fuzzy C Means Clustering For Customer Segmentation

Journal: International Journal of Scientific & Technology Research (Vol.4, No. 10)

Publication Date:

Authors : ; ; ;

Page : 70-74

Keywords : Index Terms Data Mining; Clustering; K-means; Principal component analysis; Fuzzy C means; Customer segmentation; Crisp Set;

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Abstract Data mining is the process of analyzing data and discovering useful information. Sometimes it is called knowledge Discovery. Clustering refers to groups whereas data are grouped in such a way that the data in one cluster are similar data in different clusters are dissimilar. Many data mining technologies are developed for customer segmentation. PCA is working as a preprocessor of Fuzzy C means and K- means for reducing the high dimensional and noisy data. There are many clustering method apply on customer segmentation. In this paper the performance of Fuzzy C means and K-means after implementing Principal Component Analysis is analyzed. We analyze the performance on a standard dataset for these algorithms. The results indicate that PCA based fuzzy clustering produces better results than PCA based K-means and is a more stable method for customer segmentation.

Last modified: 2015-11-13 18:33:27