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Mining Association Rules Based on Deep Pruning Strategies

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Abstract

Today mobile network and various smart devices flourish rapidly. Data collected from the mobile devices and network can bring us huge opportunities to understand some significant characteristics of the users which traditional data cannot. Association rules mining is an extremely important topic in data mining that can make the utmost value of massive data effectively. Apriori algorithm and the improved Apriori ones based on Boolean matrix are the representative ones in association rules mining. Nevertheless, these solutions have their problems. In this paper, we have proposed an algorithm called MAR-DPS, which has some deep pruning strategies containing three methods to compress the size of frequent itemsets and reduce the joining numbers in generating new frequent itemsets. It can also select the appropriate method to generate frequent 2-itemsets when facing different data sets. Extensive experimental results on three different data sets have demonstrated that our MAR-DPS can perform much better than other tested algorithms.

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Notes

  1. Mushroom Dataset: http://archive.ics.uci.edu/ml/datasets/Mushroom.

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Acknowledgements

This work was supported by the National Social Science Foundation of China under Grant 16ZDA055; National Natural Science Foundation of China under Grant 91546121, 71231002 and 61202247; EU FP7 IRSES MobileCloud Project 612212; the 111 Project of China under Grant B08004; Engineering Research Center of Information Networks, Ministry of Education; the project of Beijing Institute of Science and Technology Information; the project of CAPINFO Company Limited.

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Authors and Affiliations

  1. School of Computer, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Beijing, 100876, China

    Lei Li, Qi Li & Yabin Wu

  2. International School, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Beijing, 100876, China

    Yihang Ou

  3. CAPINFO Company Limited, No. 23 Zhichun Road, Haidian District, Beijing, 100191, China

    Daoxin Chen

Authors
  1. Lei Li
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  2. Qi Li
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  3. Yabin Wu
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  4. Yihang Ou
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  5. Daoxin Chen
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Correspondence to Lei Li.

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Li, L., Li, Q., Wu, Y. et al. Mining Association Rules Based on Deep Pruning Strategies. Wireless Pers Commun 102, 2157–2181 (2018). https://doi.org/10.1007/s11277-017-5169-0

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