Outlier Detection Based on Granular Computing

Authors:
Yuming Chen;Duoqian Miao;Ruizhi Wang
Affiliations:
Department of Computer Science and Technology, The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, P.R. China 201804;Department of Computer Science and Technology, The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, P.R. China 201804;Department of Computer Science and Technology, The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, P.R. China 201804
Venue:
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Year:
2008

Citing 14
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Abstract

As an emerging conceptual and computing paradigm of information processing, granular computing has received much attention recently. Many models and methods of granular computing have been proposed and studied. Among them was the granular computing model using information tables. In this paper, we shall demonstrate the application of this granular computing model for the study of a specific data mining problem - outlier detection. Within the granular computing model using information tables, this paper proposes a novel definition of outliers - GrC (granular computing)-based outliers. An algorithm to find such outliers is also given. And the effectiveness of GrC-based method for outlier detection is demonstrated on three publicly available databases.