Significance-based discriminative sequential pattern mining

作     者：He, Zengyou Zhang, Simeng Wu, Jun 

作者机构：Dalian Univ Technol Sch Software Tuqiang Rd Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Tuqiang Rd 321 Dalian 116600 Peoples R China Zunyi Normal Univ Sch Informat Engn Zunyi Peoples R China 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2019年第122卷

页      面：54-64页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of China Science-Technology Foundation for Youth of Guizhou Province [KY250] Fundamental Research Funds for the Central Universities [DUT2017TB02] 

主　　题：Sequential pattern Discriminative pattern Multiple hypothesis testing Family-wise error rate False discovery rate 

摘      要：Discriminative sequential patterns are sub-sequences whose occurrences exhibit significant differences across sequential data sets with different class labels. The discovery of such types of patterns has many practical applications in different fields. To date, various algorithms for mining discriminative sequential patterns have been proposed. However, the reported patterns from these methods usually contain many false positives that only hold in the sample data by chance. To alleviate this issue, we put forward the concept of significance-based discriminative sequential pattern mining and a corresponding algorithm DSPM-MTC (Discriminative Sequential Pattern Mining with Multiple Testing Correction). The key idea of DSPM-MTC is to integrate the multiple hypothesis testing correction procedure into the pattern mining process to generate a pattern set with error rate control. To demonstrate the effectiveness of DSPM-MTC, we conduct a series of experiments on real sequential data sets and simulation data sets. The experimental results show that DSPM-MTC can effectively recognize false discoveries to generate a pattern set with statistical quality control. (C) 2018 Elsevier Ltd. All rights reserved.