New outlier detection method based on fuzzy clustering

Authors:
Moh'd Belal Al-Zoubi;Ali Al-Dahoud;Abdelfatah A. Yahya
Affiliations:
Department of Computer Information Systems, University of Jordan, Amman, Jordan;Faculty of Science and IT, Al-Zaytoonah University, Amman, Jordan;Faculty of Science and IT, Al-Zaytoonah University, Amman, Jordan
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2010

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Abstract

In this paper, a new efficient method for outlier detection is proposed. The proposed method is based on fuzzy clustering techniques. The c-means algorithm is first performed, then small clusters are determined and considered as outlier clusters. Other outliers are then determined based on computing differences between objective function values when points are temporarily removed from the data set. If a noticeable change occurred on the objective function values, the points are considered outliers. Test results were performed on different well-known data sets in the data mining literature. The results showed that the proposed method gave good results.