On sparse possibilistic clustering with crispness - Classification function and sequential extraction

Hamasuna, Y. & Endo, Y. (2012). On sparse possibilistic clustering with crispness - Classification function and sequential extraction. DOI:10.1109/SCIS-ISIS.2012.6505117. In: Proceedings, 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligence Systems (ISIS), 20-24 November 2012.

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Abstract

In addition to fuzzy c-means clustering, possibilistic clustering is well-known as one of the useful techniques because it is robust against noise in data. Especially sparse possibilistic clustering is quite different from other possibilistic clustering methods in the point of membership function. We propose a way to induce the crispness in possibilistic clustering by using L1 -regularization and show classification function of sparse possibilistic clustering with crispness for understanding allocation rule. We, moreover, show the way of sequential extraction by proposed method. After that, we show the effectiveness of the proposed method through numerical examples.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords:	Classification function; L1-regularization; Possibilistic clustering; Sequential extraction
Research Programs:	Advanced Systems Analysis (ASA)
Bibliographic Reference:	In:; Proceedings, 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligence Systems (ISIS); 20-24 November 2012, Kobe, Japan pp.1801-1806
Depositing User:	IIASA Import
Date Deposited:	15 Jan 2016 08:47
Last Modified:	27 Aug 2021 17:39
URI:	https://pure.iiasa.ac.at/10178

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