A Two-Stage Clustering Algorithm for Multi-type Relational Data

Author

Gao, Ying ; Liu, Da-you ; Sun, Cheng-min ; Liu, He

Author_Institution

Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun

fYear

2008

fDate

6-8 Aug. 2008

Firstpage

376

Lastpage

380

Abstract

There are many multi-type relational datasets, the objects in which are multi-type and interrelated. Many clustering methods for this kind of data have been proposed, but because of the complexity of data and relationships, most algorithms have efficiency and scalability problem. To address this difficulty, in this paper a two-stage clustering algorithm for multi-type relational data (TSMRC) has been proposed. Based on the analysis of data and relationships, TSMRC has two stages, which are benefit to improve the efficiency of clustering. To improve the quality of clustering, new similarity measures are proposed, in which attributes and all kinds of relationships are employed. Experimental results on Movie dataset demonstrate the effectiveness of this algorithm.

Keywords

data mining; pattern clustering; relational databases; Movie dataset; data mining; multitype relational data; two-stage clustering algorithm; Artificial intelligence; Clustering algorithms; Clustering methods; Data analysis; Data mining; Distributed computing; Motion pictures; Pattern analysis; Scalability; Software engineering; clustering algorithm; multi-type relational data; two-stage method;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08. Ninth ACIS International Conference on

Conference_Location

Phuket

Print_ISBN

978-0-7695-3263-9

Type

conf

DOI

10.1109/SNPD.2008.26

Filename

4617400