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Two phase semi-supervised clustering using background knowledge
- Authors
- Shin, Kwangcheol; Abraham, Ajith
- Issue Date
- 2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS, v.4224, pp 707 - 712
- Pages
- 6
- Journal Title
- INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS
- Volume
- 4224
- Start Page
- 707
- End Page
- 712
- ISSN
- 0302-9743
- Abstract
- Using background knowledge in clustering, called semi-clustering, is one of the actively researched areas in data mining. In this paper, we illustrate how to use background knowledge related to a domain more efficiently. For a given data, the number of classes is investigated by using the must-link constraints before clustering and these must-link data are assigned to the corresponding classes. When the clustering algorithm is applied, we make use of the cannot-link constraints for assignment. The proposed clustering approach improves the result of COP k-means by about 10%.
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