Association rule mining algorithms on high-dimensional datasets

作     者：Ai, Dongmei Pan, Hongfei Li, Xiaoxin Gao, Yingxin He, Di 

作者机构：Univ Sci & Technol Beijing Sch Math & Phys 30 Xueyuan Rd Beijing 100083 Peoples R China CUNY Grad Sch Comp Sci 365 Fifth Ave New York NY 10016 USA CUNY Univ Ctr 365 Fifth Ave New York NY 10016 USA 

出 版 物：《ARTIFICIAL LIFE AND ROBOTICS》 (人工生命与机器人)

年 卷 期：2018年第23卷第3期

页      面：420-427页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China 

主　　题：Data mining algorithms Association rule mining High-dimensional datasets Frequent itemset mining 

摘      要：The science of bioinformatics has been accelerating at a fast pace, introducing more features and handling bigger volumes. However, these swift changes have, at the same time, posed challenges to data mining applications, in particular efficient association rule mining. Many data mining algorithms for high-dimensional datasets have been put forward, but the sheer numbers of these algorithms with varying features and application scenarios have complicated making suitable choices. Therefore, we present a general survey of multiple association rule mining algorithms applicable to high-dimensional datasets. The main characteristics and relative merits of these algorithms are explained, as well, pointing out areas for improvement and optimization strategies that might be better adapted to high-dimensional datasets, according to previous studies. Generally speaking, association rule mining algorithms that merge diverse optimization methods with advanced computer techniques can better balance scalability and interpretability.