A new iterative fuzzy clustering approach for incomplete data. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Goel, Sonia; Tushir, Meena
• Is Part Of: Journal of statistics & management systems. Volume 23:Issue 1(2020)
• Page Start: 91
• Page End: 102
• Publication Date: 2020-01-02
• Subjects: 62M10; FCM Clustering -- Imputation -- Missing data -- Regression -- Interpolation; Statistics -- Periodicals; Mathematical models -- Periodicals; Mathematical models; Statistics; Periodicals; 519.5
• DOI: 10.1080/09720510.2020.1714150
• Languages: English
• ISSNs: 0972-0510

Abstract

Abstract: Clustering of data with incomplete information is a difficult task, which is being researched extensively. Several researchers used fuzzy clustering methods for clustering of such data. Since conventional fuzzy clustering uses Euclidean distance metric to derive the information between a cluster center and a data point, it suffers badly when the clusters are divergent in nature. This paper proposes a new iterative Mahalanobis distance based fuzzy clustering to capture the essence of non-spherical shape of data along with incorporation of an imputation technique. The proposed methodology is compared with several imputation based FCM approaches as well as various clustering approaches as suggested by Hathway and Bezdek for incomplete data. Numerical analysis on one synthetic data set and various real life data sets show that the proposed clustering method is significantly better than other imputation as well as non-imputation techniques for these data sets.