Scaling up cosine interesting pattern discovery: A depth-first method

Abstract

This paper presents an efficient algorithm called CosMinert for interesting pattern discovery. The widely used cosine similarity, found to possess the null-invariance property and the anti-cross-support-pattern property, is adopted as the interestingness measure in CosMinert. CosMinert is generally an FP-growth-like depth-first traversal algorithm that rests on an important property of the cosine similarity: the conditional anti-monotone property (CAMP). The combined use of CAMP and the depth-first support-ascending traversal strategy enables the pre-pruning of uninteresting patterns during the mining process of CosMinert. Extensive experiments demonstrate the high efficiency of CosMinert in interesting pattern discovery, in comparison to the breath-first strategy and the post-evaluation strategy. In particular, CosMinert shows its capability in suppressing the generation of cross-support patterns and discovering rare but truly interesting patterns. Finally, an interesting case of landmark recognition is presented to illustrate the value of cosine interesting patterns found by CosMinert in real-world applications.