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Top-down Mining of Frequent Closed Patterns from Very High Dimensional Data by Hongyan Liu; Xiaoyu Wang; Jun He; Jiawei Han; Dong Xin; Zheng Shao is a Computer Science article available to read on EtoBox.

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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.

Who reads Top-down Mining of Frequent Closed Patterns from Very High Dimensional Data?

Author

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

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0020-0255)

Published

2009

Language

EN

Field

Computer Science (Physical Sciences)