Comparative Performance Of Using PCA With K-Means And Fuzzy C Means Clustering For Customer Segmentation
Fahmida Afrin, Md. Al-Amin, Mehnaz Tabassum
Index Terms: Data Mining, Clustering, K-means, Principal component analysis, Fuzzy C means, Customer segmentation, Crisp Set
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.
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