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Two-stage pruning method for gram-based categorical sequence clustering

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Abstract

Gram-based vector space model has been extensively applied to categorical sequence clustering. However, there is a general lack of an efficient method to determine the length of grams and to identify redundant and non-significant grams involved in the model. In this paper, a variable-length gram model is proposed, different from previous studies mainly focused on the fixed-length grams of sequences. The variable-length grams are obtained using a two-stage pruning method aimed at selecting the irredundant and significant subsequences from the prefix trees, created from the fixed-length grams with an initially large length. A robust partitioning algorithm is then defined for categorical sequence clustering on the normalized representation model using variable-length grams collected from the pruned trees. Experimental results on real-world sequence sets from various domains are given to demonstrate the performance of the proposed methods.

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  1. Available at http://www.ncbi.nlm.nih.gov.

  2. Available at http://pbil.univ-lyon1.fr.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant no. 61672157, and the Innovative Research Team of Probability and Statistics: Theory and Application (IRTL1704). The authors would like to thank Guiren Lian for his constructive comments that contributed to improving the paper.

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Authors and Affiliations

  1. Network Operation Maintenance Center, University of Electronic Science and Technology of China, Chengdu, China

    Liang Yuan

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, China

    Wenjian Wang

  3. Digit Fujian Internet-of-Thing Laboratory of Environment Monitoring, Fujian Normal University, Fuzhou, China

    Lifei Chen

Authors
  1. Liang Yuan
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  2. Wenjian Wang
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  3. Lifei Chen
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Correspondence to Lifei Chen.

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Yuan, L., Wang, W. & Chen, L. Two-stage pruning method for gram-based categorical sequence clustering. Int. J. Mach. Learn. & Cyber. 10, 631–640 (2019). https://doi.org/10.1007/s13042-017-0744-y

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  • DOI: https://doi.org/10.1007/s13042-017-0744-y

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