Research on the Factors of Frequent Itemset Mining Based on Dynamic Hashing

DOI

10.2991/ncce-18.2018.35How to use a DOI?

Keywords

Frequent itemsets, Dynamic hashing, Number of transaction items, The average length of the transaction, Database transaction volume, Minimum support.

Abstract

In order to improve the efficiency of association rules mining frequent itemsets, some people proposed using hashing techniques to mine association rules, it can greatly improve the time and space efficiency of frequent item sets. the space-time efficiency of the classical Apriori algorithm and the use of hashing techniques for association rule mining are analyzed, and the influence of various factors on the use of hashing techniques is analyzed. Aiming to improve the time-space efficiency of the algorithm, a dynamic hash algorithm based on data attributes is used to improve association rules mining. Experimental results verify the correctness and effectiveness of the algorithm.

Copyright

© 2018, 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  - Hanyu Hu
AU  - Yunli Chen
PY  - 2018/05
DA  - 2018/05
TI  - Research on the Factors of Frequent Itemset Mining Based on Dynamic Hashing
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 215
EP  - 220
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.35
DO  - 10.2991/ncce-18.2018.35
ID  - Hu2018/05
ER  -