A Multi-Relational Hierarchical Clustering Algorithm Based on Shared Nearest Neighbor Similarity

Author

Guo, Jing-Feng ; Zhao, Yu-Yan ; Li, Jing

Volume

7

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

3951

Lastpage

3955

Abstract

The clustering about relational databases is an active study subject in data mining. In this paper, we introduce a multi-relational hierarchical clustering algorithm based on shared nearest neighbor similarity (MHSNNS). First, this algorithm joins every table through the tuple 1D propagation. Then, groups objects into a large number of relatively small sub-clusters using the shared nearest neighbor algorithm and the cluster cohesion. Last, find the genuine clusters by repeatedly combining these sub-clusters using the cluster separation. The experiment shows the efficiency and scalability of this approach.

Keywords

data mining; hierarchical systems; pattern clustering; relational databases; cluster separation; data mining; multirelational hierarchical clustering; relational databases; shared nearest neighbor similarity; Clustering algorithms; Cybernetics; Data mining; Educational institutions; Machine learning; Machine learning algorithms; Nearest neighbor searches; Partitioning algorithms; Relational databases; Scalability; Data mining; Hierarchical clustering; Multi-relational clustering; Relational databases; Shared nearest neighbor;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370836

Filename

4370836