NDLI: Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

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Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

Content Provider	ACM Digital Library
Author	Nakagawa, Kazuya Karasuyama, Masayuki Takeuchi, Ichiro Tsuda, Koji Suzumura, Shinya
Abstract	In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model. The advantage of the SPP method over existing boosting-type method is that the former can find the superset by a single search over the database, while the latter requires multiple searches. The SPP method is inspired by recent development of safe feature screening. In order to extend the idea of safe feature screening into predictive pattern mining, we derive a novel pruning rule called safe pattern pruning (SPP) rule that can be used for searching over the tree defined among patterns in the database. The SPP rule has a property that, if a node corresponding to a pattern in the database is pruned out by the SPP rule, then it is guaranteed that all the patterns corresponding to its descendant nodes are never needed for the optimal predictive model. We apply the SPP method to graph mining and item-set mining problems, and demonstrate its computational advantage.
Starting Page	1785
Ending Page	1794
Page Count	10
File Format	PDF
ISBN	9781450342322
DOI	10.1145/2939672.2939844
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Safe screening Convex optimization Item-set mining Sparse learning Predictive pattern mining Graph mining
Content Type	Text
Resource Type	Article

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