Association Rule Classification and Regression Algorithm Based on Frequent Itemset Tree

DOI

10.2991/msam-18.2018.30How to use a DOI?

Keywords

matrix operation; frequent itemset tree; association rule; classification; regression

Abstract

The categorization association rules based on the Apriori algorithm can’t deal with the numerical data directly. When mass rules are generated, classifying the new data enjoys matching so many rules one by one as to decrease the efficiency and accuracy. Moreover, the association rules can’t be used to realize the regression prediction. In order to solve above problems, we proposed a new association rule classification and regression algorithm based on frequent itemset tree (ARCRFI-tree) according to the advantages of matrix operation and tree structure. Firstly, all frequent itemsets are obtained by constructing a new frequent tree structure, based on which the association rules are mined. Then, the consequents of the association rules are reconstructed with the least square method to realize the classification and regression prediction for new sample. Finally, the theoretical analysis and experiments compared with algorithms demonstrate our algorithm has high prediction accuracy and mining efficiency.

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  - Ling Wang
AU  - Hui Zhu
AU  - Ruixia Huang
PY  - 2018/07
DA  - 2018/07
TI  - Association Rule Classification and Regression Algorithm Based on Frequent Itemset Tree
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 133
EP  - 139
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.30
DO  - 10.2991/msam-18.2018.30
ID  - Wang2018/07
ER  -