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) |
---|---|
Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Pages | 1590-1595 |
Number of pages | 6 |
Volume | 2 |
State | Published - Jan 1 2002 |
Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002 |
Other
Other | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
---|---|
Country | United States |
City | Honolulu, HI |
Period | 5/12/02 → 5/17/02 |