Volume 9 Number 3 (Mar. 2014)
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JSW 2014 Vol.9(3): 732-737 ISSN: 1796-217X
doi: 10.4304/jsw.9.3.732-737

CUDAP: A Novel Clustering Algorithm for Uncertain Data Based on Approximate Backbone

Ping Jin1, 2, Shichao Qu2, 3, Yu Zong1, Xin Li2
1Information and Engineering School, West Anhui University, Luan, China
2School of Computer Science, University of Science and Technology of China, Hefei, China
3School of Software, Dalian University of Technology, Dalian, China


Abstract—Clustering for uncertain data is an interesting research topic in data mining. Researchers prefer to define uncertain data clustering problem by using combinatorial optimization model. Heuristic clustering algorithm is an efficient way to deal with this kind of clustering problem, but initialization sensitivity is one of inevitable drawbacks. In this paper, we propose a novel clustering algorithm named CUDAP (Clustering algorithm for Uncertain Data based on Approximate backbone). In CUDAP, we (1) make M times random sampling on the original uncertain data set Dm to generate M sampled data sets DS={Ds1,Ds2,…,DsM}; (2) capture the M local optimal clustering results P={C1,C2,…,CM} from DS by running UK-Medoids algorithm on each sample data set Dsi, i=1,…M; (3) design a greedy search algorithm to find out the approximate backbone(APB) from P; (4) run UK-Medoids again on the original uncertain data set Dm guided by new initialization which was generated from APB. Experimental results on synthetic and real world data sets demonstrate the superiority of the proposed approach in terms of clustering quality measures.

Index Terms—NP-hard Problem; Uncertain Data Clustering Problem; Heuristic Clustering Algorithm; Approximate Backbone

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Cite: Ping Jin, Shichao Qu, Yu Zong, Xin Li, "CUDAP: A Novel Clustering Algorithm for Uncertain Data Based on Approximate Backbone," Journal of Software vol. 9, no. 3, pp. 732-737, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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