Top-down mining of frequent closed patterns from very high dimensional data

  • Authors:

  • Hongyan Liu;Xiaoyu Wang;Jun He;Jiawei Han;Dong Xin;Zheng Shao

  • Affiliations:

  • Department of Management Science and Engineering, Tsinghua University, Beijing 100084, China;Department of Management Science and Engineering, Tsinghua University, Beijing 100084, China;Department of Computer Science, Renmin University of China, Beijing 100872, China;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

Frequent pattern mining is an essential theme in data mining. Existing algorithms usually use a bottom-up search strategy. However, for very high dimensional data, this strategy cannot fully utilize the minimum support constraint to prune the rowset search space. In this paper, we propose a new method called top-down mining together with a novel row enumeration tree to make full use of the pruning power of the minimum support constraint. Furthermore, to efficiently check if a rowset is closed, we develop a method called the trace-based method. Based on these methods, an algorithm called TD-Close is designed for mining a complete set of frequent closed patterns. To enhance its performance further, we improve it by using new pruning strategies and new data structures that lead to a new algorithm TTD-Close. Our performance study shows that the top-down strategy is effective in cutting down search space and saving memory space, while the trace-based method facilitates the closeness-checking. As a result, the algorithm TTD-Close outperforms the bottom-up search algorithms such as Carpenter and FPclose in most cases. It also runs faster than TD-Close.