High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to improve the efficiency of hierarchical document clustering. In this paper, we introduce the notion of "closed interesting" itemsets (i.e. closed itemsets with high interestingness). We provide heuristics such as "super item" to efficiently mine these itemsets and show that they provide significant dimensionality reduction over closed frequent itemsets.

Using "closed interesting" itemsets, we propose a new, sub-linearly scalable, hierarchical document clustering method that outperforms state of the art agglomerative, partitioning and frequent-itemset based methods both in terms of clustering quality and runtime performance, without requiring dataset specific parameter tuning. We evaluate twenty interestingness measures and show that when used to generate "closed interesting" itemsets, and to select parent nodes, Mutual Information, Added Value, Yule?s Q and Chi- Square offer best clustering performance.