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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 6978146, 11 pages
Research Article

An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

1College of Management Science and Engineering, Shandong Normal University, Jinan, Shandong, China
2College of Business, The University of Texas at San Antonio, San Antonio, TX, USA

Correspondence should be addressed to Yuzhen Zhao

Received 4 November 2016; Revised 6 January 2017; Accepted 30 January 2017; Published 19 February 2017

Academic Editor: Stefan Balint

Copyright © 2017 Xiyu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Apriori algorithm, as a typical frequent itemsets mining method, can help researchers and practitioners discover implicit associations from large amounts of data. In this work, a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets, is proposed, which is based on an evolution-communication tissue-like P system with promoters and inhibitors. The structure of the ECTPPI-Apriori algorithm is tissue-like and the evolution rules of the algorithm are object rewriting rules. The time complexity of ECTPPI-Apriori is substantially improved from that of the conventional Apriori algorithms. The results give some hints to improve conventional algorithms by using membrane computing models.