DocumentCode
447568
Title
Consideration on hierarchical cluster analysis based on connecting adjacent hyper-rectangles
Author
Yanagida, Ryoshin ; Takagi, Noboru
Author_Institution
Dept. of Electron. & Informatics, Toyama Prefectural Univ., Japan
Volume
3
fYear
2005
fDate
10-12 Oct. 2005
Firstpage
2795
Abstract
This paper proposes a new clustering method based on connecting adjacent hyper-rectangles. The k-means clustering is one of the well-known clustering techniques. At first, many clustering methods must decide the number of clusters. Our method searches a set of hyper-rectangles that satisfies the properties (1) each hyper-rectangle covers some of the samples, and (2) each sample is covered by at least one of the hyper-rectangles. Then, a collection of connected hyper-rectangles is assumed to be a cluster. One of the characteristic features of our method is that it can work if there is no initial value on the number of clusters assumed. We apply the hierarchical clustering method to realize the clustering based on connecting adjacent hyper-rectangles. The effectiveness of the, proposed method is shown by applying a small artificial data and iris data.
Keywords
computational geometry; pattern clustering; sampling methods; unsupervised learning; adjacent hyper-rectangles; hierarchical cluster analysis; iris data; k-means clustering; Clustering algorithms; Clustering methods; Informatics; Iris; Joining processes; Optimization methods; Pattern analysis; Pattern classification; Rough sets; Statistical analysis; clustering; combinatorial optimization problem; hierarchical clustering; pattern classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 2005 IEEE International Conference on
Print_ISBN
0-7803-9298-1
Type
conf
DOI
10.1109/ICSMC.2005.1571573
Filename
1571573
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