NDLI: HISSCLU: a hierarchical density-based method for semi-supervised clustering

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HISSCLU: a hierarchical density-based method for semi-supervised clustering

Content Provider	ACM Digital Library
Author	Plant, Claudia Böhm, Christian
Abstract	In situations where class labels are known for a part of the objects, a cluster analysis respecting this information, i.e. semi-supervised clustering, can give insight into the class and cluster structure of a data set. Several semi-supervised clustering algorithms such as HMRF-K-Means [4], COP-K-Means [26] and the CCL-algorithm [18] have recently been proposed. Most of them extend well-known clustering methods (K-Means [22], Complete Link [17] by enforcing two types of constraints: must-links between objects of the same class and cannot-links between objects of different classes. In this paper, we propose HISSCLU, a hierarchical, density-based method for semi-supervised clustering. Instead of deriving explicit constraints from the labeled objects, HISSCLU expands the clusters starting at all labeled objects simultaneously. During the expansion, class labels are assigned to the unlabeled objects most consistently with the cluster structure. Using this information the hierarchical cluster structure is determined. The result is visualized in a semi-supervised cluster diagram showing both cluster structure as well as class assignment. Compared to methods based on must-links and cannot-links, our method allows a better preservation of the actual cluster structure, particularly if the data set contains several distinct clusters of the same class (i.e. the intra-class data distribution is multimodal). HISSCLU has a determinate result, is efficient and robust against noise. The performance of our algorithm is shown in an extensive experimental evaluation on synthetic and real-world data sets.
Starting Page	440
Ending Page	451
Page Count	12
File Format	PDF
ISBN	9781595939265
DOI	10.1145/1353343.1353398
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-03-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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