Association Rule Mining Algorithm of Transposed matrix Based on Python Language

DOI

10.2991/snce-18.2018.59How to use a DOI?

Keywords

Transposed matrix; Association rules; Data mining; Python language; NumPy

Abstract

Apriori and its improved algorithms can be generally classified into two kinds: SQL-based and on memory-based. In order to improve association rule mining efficiency, after analyzing the efficiency bottlenecks in some algorithms of the second class, an improved efficient algorithm for Python language is proposed. Two matrixes are introduced into the algorithm: one is used to map database and the other to store frequent 2-itemsets related information. Through the operation of two matrixes, its time complexity and space complexity decrease significantly. The experiment indicates that the method has better performance.

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  - Shaoyun Song
PY  - 2018/05
DA  - 2018/05
TI  - Association Rule Mining Algorithm of Transposed matrix Based on Python Language
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 297
EP  - 303
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.59
DO  - 10.2991/snce-18.2018.59
ID  - Song2018/05
ER  -