Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
536253 | Pattern Recognition Letters | 2006 | 5 Pages |
Abstract
This paper introduces a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental results show that it produces meaningful cluster abstractions for interval data at a minimal computational expense.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
S. Asharaf, M. Narasimha Murty, S.K. Shevade,