DocumentCode :
437478
Title :
MinClue: a MST-based clustering method with auto-threshold-detection
Author :
He, Yu. ; Chen, Lihui
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume :
1
fYear :
2004
fDate :
1-3 Dec. 2004
Firstpage :
229
Abstract :
Clustering is to group data points into homogenous clusters so that data points within the same cluster are more similar than data points belonging to different clusters. There are many effective clustering algorithms for discovering arbitrary shaped clusters, but one common problem of many algorithms is the difficulty for users to decide appropriate parameters for these algorithms. To reduce the dependence of clustering performance on parameters, this paper proposes a threshold criterion for the single linkage cluster analysis and incorporates it into the Minimum Spanning Tree (MST) based clustering method. Since the threshold can be automatically decided according to the underlying data distributions, arbitrary shaped clusters can be discovered with little human intervention. The experimental results on spatial data are very encouraging.
Keywords :
data mining; pattern clustering; tree searching; MST-based clustering method; auto-threshold-detection; group data points; minimum spanning tree; Clustering algorithms; Clustering methods; Couplings; Data engineering; Helium; Humans; Iterative algorithms; Partitioning algorithms; Performance analysis; Tree graphs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2004 IEEE Conference on
Print_ISBN :
0-7803-8643-4
Type :
conf
DOI :
10.1109/ICCIS.2004.1460417
Filename :
1460417
Link To Document :
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