Frequency sensitive competitive learning for clustering on high-dimensional hyperspheres

Author

Banerjee, Arindam ; Ghosh, Joydeep

Author_Institution

Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1590

Lastpage

1595

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

Keywords

data handling; maximum likelihood estimation; neural nets; pattern clustering; unsupervised learning; balanced grouping; competitive learning; frequency sensitive competitive algorithm; high dimensional text data sets; high-dimensional hypersphere; maximum likelihood estimation; spherical k-means algorithm; text clustering; winner take-all networks; Clustering algorithms; Euclidean distance; Frequency; Hebbian theory; Learning systems; Power capacitors; Resource management; Stability; Subspace constraints; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007755

Filename

1007755