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

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-KMeans [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, densitybased 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 clust...

• Type: Conference
• Year: 2008
• Where: EDBT
• Authors: Christian Böhm, Claudia Plant
• Keywords: Cluster Structure, Database, EDBT 2008, Hierarchical Cluster Structure, Semi-supervised Cluster Diagram