2008 DirectMiningofDiscriminativeand

(Fan et al., 2008) ⇒ Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Yan, Jiawei Han, Philip Yu, and Olivier Verscheure. (2008). “Direct Mining of Discriminative and Essential Frequent Patterns via Model-based Search Tree.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401922

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Abstract

Frequent patterns provide solutions to datasets that do not have well-structured feature vectors. However, frequent pattern mining is non-trivial since the number of unique patterns is exponential but many are non-discriminative and correlated. Currently, frequent pattern mining is performed in two sequential steps: enumerating a set of frequent patterns, followed by feature selection. Although many methods have been proposed in the past few years on how to perform each separate step efficiently, there is still limited success in eventually finding highly compact and discriminative patterns. The culprit is due to the inherent nature of this widely adopted two-step approach. This paper discusses these problems and proposes a new and different method. It builds a decision tree that partitions the data onto different nodes. Then at each node, it directly discovers a discriminative pattern to further divide its examples into purer subsets. Since the number of examples towards leaf level is relatively small, the new approach is able to examine patterns with extremely low global support that could not be enumerated on the whole dataset by the two-step method. The discovered feature vectors are more accurate on some of the most difficult graph as well as frequent itemset problems than most recently proposed algorithms but the total size is typically 50% or more smaller. Importantly, the minimum support of some discriminative patterns can be extremely low (e.g. 0.03%). In order to enumerate these low support patterns, state-of-the-art frequent pattern algorithm either cannot finish due to huge memory consumption or have to enumerate 10¹ to 10³ times more patterns before they can even be found. Software and datasets are available by contacting the author.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 DirectMiningofDiscriminativeand	Jing Gao Wei Fan Philip S. Yu Hong Cheng Xifeng Yan Kun Zhang Olivier Verscheure Jiawei Han			Direct Mining of Discriminative and Essential Frequent Patterns via Model-based Search Tree				10.1145/1401890.1401922