Title: On the Techniques for Data Clustering with Numerical Constraints
Abstract: Previous chapter Next chapter Full AccessProceedings Proceedings of the 2003 SIAM International Conference on Data Mining (SDM)On the Techniques for Data Clustering with Numerical ConstraintsBi-Ru Dai, Cheng-Ru Lin, and Ming-Syan ChenBi-Ru Dai, Cheng-Ru Lin, and Ming-Syan Chenpp.264 - 268Chapter DOI:https://doi.org/10.1137/1.9781611972733.27PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, the attributes employed to model the constraints are called constraint attributes and those attributes involved in the objective function to be optimized are called cost-optimal attributes. The constrained clustering considered is conducted in such a way that the objective function of cost-optimal attributes is optimized subject to the condition that the imposed constraint is satisfied. Explicitly, we address the problem of constrained clustering with numerical constraints, in which the constraint attribute values of any two data items in the same cluster are required to be within the corresponding constraint range. We devise an effective and efficient algorithm with complete-link to solve this clustering problem. It is noted that due to the intrinsic nature of the numerical constrained clustering, there is an order dependency on the process of attaining the clustering, which in many cases degrades the clustering results. In view of this, we devise a progressive constraint relaxation technique to remedy this drawback and improve the overall performance of clustering results. Explicitly, by using a smaller (tighter) constraint range in earlier iterations of merge, we will have more room to relax the constraint and seek for better solutions in subsequent iterations. It is empirically shown that the progressive constraint relaxation technique is able to improve not only the execution efficiency but also the clustering quality. Previous chapter Next chapter RelatedDetails Published:2003ISBN:978-0-89871-545-3eISBN:978-1-61197-273-3 https://doi.org/10.1137/1.9781611972733Book Series Name:ProceedingsBook Code:PR112Book Pages:xiv + 347Key words:Data Mining, data clustering, constrained clustering
Publication Year: 2003
Publication Date: 2003-05-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 9
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