Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
482468 | European Journal of Operational Research | 2006 | 15 Pages |
Abstract
Clustering is the process of grouping a set of objects into classes of similar objects. In the past, clustering algorithms had a common problem that they use only one set of attributes for both partitioning the data space and measuring the similarity between objects. This problem has limited the use of the existing algorithms on some practical situation. Hence, this paper introduces a new clustering algorithm, which partitions data space by constructing a decision tree using one attribute set, and measures the degree of similarity using another. Three different partitioning methods are presented. The algorithm is explained with illustration. The performance and accuracy of the four partitioning methods are evaluated and compared.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Yen-Liang Chen, Wu-Hsien Hsu, Yu-Hsuan Lee,