Frequency sensitive competitive learning for clustering on high-dimensional hyperspheres

Arindam Banerjee; Joydeep Ghosh

Frequency sensitive competitive learning for clustering on high-dimensional hyperspheres

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper derives three competitive learning mechanisms from first principles to obtain clusters of comparable sizes when both inputs and representatives are normalized. These mechanisms are very effective in achieving balanced grouping of inputs in high dimensional spaces, as illustrated by experimental results on clustering two popular text data sets in 26,099 and 21,839 dimensional spaces respectively.

Original language	English (US)
Pages	1590-1595
Number of pages	6
State	Published - 2002
Externally published	Yes
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002

Conference

Conference	2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/Territory	United States
City	Honolulu, HI
Period	5/12/02 → 5/17/02

ASJC Scopus subject areas

Software
Artificial Intelligence

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abstract = "This paper derives three competitive learning mechanisms from first principles to obtain clusters of comparable sizes when both inputs and representatives are normalized. These mechanisms are very effective in achieving balanced grouping of inputs in high dimensional spaces, as illustrated by experimental results on clustering two popular text data sets in 26,099 and 21,839 dimensional spaces respectively.",

author = "Arindam Banerjee and Joydeep Ghosh",

note = "Copyright: Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.; 2002 International Joint Conference on Neural Networks (IJCNN '02) ; Conference date: 12-05-2002 Through 17-05-2002",

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T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)

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Frequency sensitive competitive learning for clustering on high-dimensional hyperspheres

Abstract

Conference

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