Association Rule Classification and Regression Algorithm Based on Frequent Itemset Tree
- 10.2991/msam-18.2018.30How to use a DOI?
- matrix operation; frequent itemset tree; association rule; classification; regression
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.
- © 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/).
Cite this article
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 -