Improvement of Apriori Algorithm Based on Matrix Compression

DOI

10.2991/emim-17.2017.28How to use a DOI?

Keywords

Data mining; Time; Improvement; Relational matrix; Association rule

Abstract

The traditional association rule mining algorithm for Apriori time cost, the lack of Apriori algorithm, based on the theory of relational algebra, relation matrix and related operations by given search association rules from the frequent itemsets mining algorithm based on relation algebra theory. Using the relation matrix to scan the database only once, in order to reduce the running time of the algorithm, frequent itemsets mining, finally the simulation results comparing the two execution time of the algorithm, the effect of sample data and the minimum support degree on the performance of the algorithm is discussed. The simulation results show that the improved algorithm is efficient and reduces the running time of mining frequent itemsets.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Jigang Zheng
AU  - Jingmei Zhang
PY  - 2017/04
DA  - 2017/04
TI  - Improvement of Apriori Algorithm Based on Matrix Compression
BT  - Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)
PB  - Atlantis Press
SP  - 131
EP  - 135
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-17.2017.28
DO  - 10.2991/emim-17.2017.28
ID  - Zheng2017/04
ER  -