Presentation 1998/6/19
Hierarchical Clustering by Extended Mean Shift
Toru WAKAHARA, Toshiaki SUGIMURA,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Mean shift, a simple iterative clustering procedure that shifts each data point to the average of data points in its neighborhood, is strongly extended in three ways. First, the window parameter controlling the size of the neighborhood is automatically determined from the original distribution of points. Second, stable, hierarchical clustering is achieved by increasing the window parameter value in a deterministic manner. Third, the guarantee of its covergence is rigorously proven. A critical comparison of the extended mean shift method to the k-means method and other hierarchical clustering methods is made using artificial 2D point distributions of overlapping Gaussians and half-circles with random noise in order to show its ability for realizing both normal decomposition and chaining effect. Moreover, the limits of the extended mean shift method's abilities are discussed by discriminating between cluster extraction and cluster representation problems.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Extended mean shift / hierarchical clustering / normal decomposition / chaining effect / cluster extraction/representation
Paper # PRMU98-38
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Conference Information
Committee PRMU
Conference Date 1998/6/19(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Hierarchical Clustering by Extended Mean Shift
Sub Title (in English)
Keyword(1) Extended mean shift
Keyword(2) hierarchical clustering
Keyword(3) normal decomposition
Keyword(4) chaining effect
Keyword(5) cluster extraction/representation
1st Author's Name Toru WAKAHARA
1st Author's Affiliation NTT Human Interface Laboratories()
2nd Author's Name Toshiaki SUGIMURA
2nd Author's Affiliation NTT Human Interface Laboratories
Date 1998/6/19
Paper # PRMU98-38
Volume (vol) vol.98
Number (no) 127
Page pp.pp.-
#Pages 8
Date of Issue