The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 89-515
Full Name: Ali Al-Dahoud
Position: Associate Professor
Age: ON
Sex: Male
Address: Al-Zaytoonah University, IT Faculty, Amman
Country: JORDAN
Tel: 00962795323223
Tel prefix:
Fax: 0096264291432
E-mail address: aldahoud@alzaytoonah.edu.jo
Other E-mails:
Title of the Paper: New Outlier Detection Method Based on Fuzzy Clustering
Authors as they appear in the Paper: Moh'd Belal Al-Zoubi, Ali Al-Dahoud, Abdelfatah A. Yahya
Email addresses of all the authors: mba@ju.edu.jo, aldahoud@alzaytoonah.edu.jo, science@alzaytoonah.edu.jo
Number of paper pages: 10
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.
Keywords: Outlier detection, Data mining, Clustering, Fuzzy clustering, FCM algorithm, Noise removal
EXTENSION of the file: .doc
Special (Invited) Session: Fuzzy Clustering-Based Approach for Outlier Detection
Organizer of the Session: 642-326
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IP ADDRESS: 213.186.168.146
