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Frequent pattern mining: current status and future directions

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Abstract

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.

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  1. Department of Computer Science, University of Illinois, 1304 West Springfield Ave, Urbana, IL, 61801, USA

    Jiawei Han, Hong Cheng, Dong Xin & Xifeng Yan

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  1. Jiawei Han
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  2. Hong Cheng
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  3. Dong Xin
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  4. Xifeng Yan
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Correspondence to Jiawei Han.

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Communicated by Geoff Webb.

The work was supported in part by the U.S. National Science Foundation NSF IIS-05-13678/06-42771 and NSF BDI-05-15813. Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funding agencies.

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Han, J., Cheng, H., Xin, D. et al. Frequent pattern mining: current status and future directions. Data Min Knowl Disc 15, 55–86 (2007). https://doi.org/10.1007/s10618-006-0059-1

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