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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

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Online
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2083-2567
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A New Approach For Discovering Top-K Sequential Patterns Based On The Variety Of Items

Shigeaki Sakurai
  • Big Data Cloud Technology Center, Toshiba Corporation Cloud & Solutions Company, 72-34, Horikawa-cho, Saiwai-ku, Kawasaki 212-8585, Japan
/ Minoru Nishizawa
  • Advanced IT Research and Development Center, Toshiba Solutions Company, 3-22, Katamachi, Fuchu, Tokyo 183-8512, Japan
Published Online: 2015-05-26 | DOI: https://doi.org/10.1515/jaiscr-2015-0025

Abstract

This paper proposes a method that discovers various sequential patterns from sequential data. The sequential data is a set of sequences. Each sequence is a row of item sets. Many previous methods discover frequent sequential patterns from the data. However, the patterns tend to be similar to each other because they are composed of limited items. The patterns do not always correspond to the interests of analysts. Therefore, this paper tackles on the issue discovering various sequential patterns. The proposed method decides redundant sequential patterns by evaluating the variety of items and deletes them based on three kinds of delete processes. It can discover various sequential patterns within the upper bound for the number of sequential patterns given by the analysts. This paper applies the method to the synthetic sequential data which is characterized by number of items, their kind, and length of sequence. The effect of the method is verified through numerical experiments.

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About the article

Published Online: 2015-05-26

Published in Print: 2015-04-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0025.

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© Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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