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Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 31-559
Full Name: Hung-Pin Chiu
Position: Assistant Professor
Age: ON
Sex: Male
Address: No.32, Chung Keng Li, Dalin ChiaYi, 622
Country: TAIWAN
Tel: 886-5-2721001 ext. 50205
Tel prefix:
Fax: 886-5-2427137
E-mail address: hpchiu@mail.nhu.edu.tw
Other E-mails: hpchiu204@yahoo.com.tw
Title of the Paper: a novel approach for missing data processing based on compounded PSO clustering
Authors as they appear in the Paper: Hung-Pin Chiu, Tsen-Jen Wei, Hsiang-Yi Lee
Email addresses of all the authors: hpchiu@mail.nhu.edu.tw, angle2567@yahoo.com.tw, hylee@mail.nhu.edu.tw
Number of paper pages: 12
Abstract: Incomplete and noisy data significantly distort data mining results. Therefore, taking care of missing values or noisy data becomes extremely crucial in data mining. Recent researches start to exploit data clustering techniques to estimate missing values. Obviously the quality of clustering analysis significantly influences the performance of missing data estimation. It was proven that clustering problem is NP-hard. Particle swarm optimization (PSO) is the recently suggested heuristic search process for solving data clustering problems. In this paper, a compounded PSO (CPSO) clustering approach is proposed for the missing value estimation. Normalization methods are first utilized to filter outliers and prevent some attributes from dominating the clustering result. Then the K-means algorithm and reflex mechanism are combined with the standard PSO clustering so that it can quickly converge to a reasonable good solution. Meanwhile, an iteration-based filling-in value !
scheme is utilized to guide the searching of CPSO clustering for the optimal estimate values. Effectiveness of the proposed approach is demonstrated on some data sets for four different rates of missing data. The empirical evaluation shows the superiority of CPSO over the well known K-means, PSO, and SOM-based approaches, and it is desirable for solving missing value problems.
Keywords: Particle swarm optimization, Data clustering, Missing values, Iteration-based filling-in scheme
EXTENSION of the file: .pdf
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