document: 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 driven by ontology significantly improve the clustering performance with minimal degradation of the cluster quality . Meanwhile , many hierarchical organizations of gene clusters corresponding to a sub-hierarchies in gene ontology were also successfully captured .
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