A survey of itemset mining

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently co-occurring in databases. Due to its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular research area. This paper provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining and uncertain itemset mining. The paper also discusses research opportunities and the relationship to other popular pattern mining problems such as sequential pattern mining, episode mining, sub-graph mining and association rule ∗School of Natural Science and Humanities, Harbin Institute of Technology Shenzhen Graduate School, China †School of Computer Science and Technology, University 2, Harbin Institute of Technology Shenzhen Graduate School, China ‡Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam §College of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea ¶Department of Mathematics and Informatics, University of Dalat, Vietnam ‖Faculty of Health, Engineering and Sciences, University of Southern Queensland, Australia ∗∗Faculty of Information Technology, University of Science, Vietnam