Data Mining and Knowledge Discovery

, Volume 15, Issue 1, pp 55-86

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Frequent pattern mining: current status and future directions

  • Jiawei HanAffiliated withDepartment of Computer Science, University of Illinois Email author 
  • , Hong ChengAffiliated withDepartment of Computer Science, University of Illinois
  • , Dong XinAffiliated withDepartment of Computer Science, University of Illinois
  • , Xifeng YanAffiliated withDepartment of Computer Science, University of Illinois


Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.


Frequent pattern mining Association rules Data mining research Applications