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Mining sequential patterns with periodic wildcard gaps

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Abstract

Mining frequent patterns with periodic wildcard gaps is a critical data mining problem to deal with complex real-world problems. This problem can be described as follows: given a subject sequence, a pre-specified threshold, and a variable gap-length with wildcards between each two consecutive letters. The task is to gain all frequent patterns with periodic wildcard gaps. State-of-the-art mining algorithms which use matrices or other linear data structures to solve the problem not only consume a large amount of memory but also run slowly. In this study, we use an Incomplete Nettree structure (the last layer of a Nettree which is an extension of a tree) of a sub-pattern P to efficiently create Incomplete Nettrees of all its super-patterns with prefix pattern P and compute the numbers of their supports in a one-way scan. We propose two new algorithms, MAPB (Mining sequentiAl Pattern using incomplete Nettree with Breadth first search) and MAPD (Mining sequentiAl Pattern using incomplete Nettree with Depth first search), to solve the problem effectively with low memory requirements. Furthermore, we design a heuristic algorithm MAPBOK (MAPB for tOp-K) based on MAPB to deal with the Top-K frequent patterns for each length. Experimental results on real-world biological data demonstrate the superiority of the proposed algorithms in running time and space consumption and also show that the pattern matching approach can be employed to mine special frequent patterns effectively.

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Acknowledgements

This research is supported by the National Natural Foundation of China under grants No. 61229301, 61170190, and 61370144, the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China, under grant IRT13059, the National 863 Program of China under grant 2012AA011005, the National 973 Program of China under grant 2013CB329604, the Natural Science Foundation of Hebei Province of China under grant No. F2013202138, the Key Project of the Educational Commission of Hebei Province under grant No. ZH2012038, and the Industrial Science and Technology Pillar Program of Changzhou, Jiangsu, China, under grant CE20120026.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Hebei University of Technology, Tianjin, 300130, China

    Youxi Wu & Lingling Wang

  2. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Jiadong Ren

  3. Department of Computer Science, University of Massachusetts Boston, Boston, 02125, USA

    Wei Ding

  4. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Xindong Wu

  5. Department of Computer Science, University of Vermont, Burlington, VT, 05405, USA

    Xindong Wu

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  1. Youxi Wu
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  2. Lingling Wang
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  3. Jiadong Ren
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  4. Wei Ding
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Correspondence to Youxi Wu.

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Wu, Y., Wang, L., Ren, J. et al. Mining sequential patterns with periodic wildcard gaps. Appl Intell 41, 99–116 (2014). https://doi.org/10.1007/s10489-013-0499-4

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