Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Journal of Classification
A new possibilistic clustering method: the possibilistic K-modes
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Correlating Fuzzy and Rough Clustering
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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Clustering categorical data sets under uncertain framework is a fundamental task in data mining area. In this paper, we propose a new method based on the k-modes clustering method using rough set and possibility theories in order to cluster objects into several clusters. While possibility theory handles the uncertainty in the belonging of objects to different clusters by specifying the possibilistic membership degrees, rough set theory detects and clusters peripheral objects using the upper and lower approximations. We introduce modifications on the standard version of the k-modes approach (SKM) to obtain the rough possibilistic k-modes method denoted by RPKM. These modifications make it possible to classify objects to different clusters characterized by rough boundaries. Experimental results on benchmark UCI data sets indicate the effectiveness of our proposed method i.e. RPKM.