A framework for ontology-driven subspace clustering

Traditional clustering is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. While domain knowledge is always the best way to justify clustering, few clustering algorithms have ever take domain knowledge into consideration. In this paper, the domain knowledge is represented by hierarchical ontology. We develop a framework by directly incorporating domain knowledge into clustering process, yielding a set of clusters with strong ontology implication. During the clustering process, ontology information is utilized to efficiently prune the exponential search space of the subspace clustering algorithms. Meanwhile, the algorithm generates automatical interpretation of the clustering result by mapping the natural hierarchical organized subspace clusters with significant categorical enrichment onto the ontology hierarchy. Our experiments on a set of gene expression data using gene ontology demonstrate that our pruning technique dri...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Jinze Liu, Wei Wang 0010, Jiong Yang
• Keywords: Data Mining, Gene Ontology Demonstrate, Hierarchical Ontology, KDD 2004, Subspace Clustering Algorithms