SafePatternPruning: An Efficient Approach for Predictive Pattern Mining (KDD'16)

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 pat- tern pruning (SPP) for a class of predictive pattern mining problems. The SPP method allows us to efficiently nd 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 nd 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 fea- ture 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 dened 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.

Environmental Requirement

• gcc version 4.8.4
• GNU Make 3.81

How to Compile

cd graphLasso (or graphSVM, itemLasso, itemSVM)
make

Usage

./train [option] [filename]

option

• -T : compute regularization path for a sequence of T \lambda evenly allocated between \lambda_0 and 0.01\lambda

• -F : calculate duality gap and dynamic screening every F iteration

• -S : minimum support (graph only)

• -D : maxdepth or maxpat

• -B : with bias (0 or 1)

Example

./train -T 100 -F 50 -D 3 -B 1 ./data/cpdb

Lisence

GNU General Public License