A New Algorithm of Mining High Utility Sequential Pattern in Streaming Data

DOI

10.2991/ijcis.2019.125905650How to use a DOI?

Keywords

High utility sequential pattern; Data streaming; Sliding windows; Tree structure; Header table

Abstract

High utility sequential pattern (HUSP) mining has emerged as a novel topic in data mining, its computational complexity increases compared to frequent sequences mining and high utility itemsets mining. A number of algorithms have been proposed to solve such problem, but they mainly focus on mining HUSP in static databases and do not take streaming data into account, where unbounded data come continuously and often at a high speed. The efficiency of mining algorithms is still the main research topic in this field. In view of this, this paper proposes an efficient HUSP mining algorithm named HUSP-UT (utility on Tail Tree) based on tree structure over data stream. Substantial experiments on real datasets show that HUSP-UT identifies high utility sequences efficiently. Comparing with the state-of-the-art algorithm HUSP-Stream (HUSP mining over data streams) in our experiments, the proposed HUSP-UT outperformed its counterpart significantly, especially for time efficiency, which was up to 1 order of magnitude faster on some datasets.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Huijun Tang
AU  - Yangguang Liu
AU  - Le Wang
PY  - 2019
DA  - 2019/01/28
TI  - A New Algorithm of Mining High Utility Sequential Pattern in Streaming Data
JO  - International Journal of Computational Intelligence Systems
SP  - 342
EP  - 350
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2019.125905650
DO  - 10.2991/ijcis.2019.125905650
ID  - Tang2019
ER  -