Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
403012 | Knowledge-Based Systems | 2010 | 7 Pages |
Abstract
Data clustering is an important and frequently used unsupervised learning method. Recent research has demonstrated that incorporating instance-level background information to traditional clustering algorithms can increase the clustering performance. In this paper, we extend traditional clustering by introducing additional prior knowledge such as the size of each cluster. We propose a heuristic algorithm to transform size constrained clustering problems into integer linear programming problems. Experiments on both synthetic and UCI datasets demonstrate that our proposed approach can utilize cluster size constraints and lead to the improvement of clustering accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Shunzhi Zhu, Dingding Wang, Tao Li,