Title :
Symmetry-Based Pruning in Itemset Mining
Author :
Jabbour, Said ; Khiari, Mehdi ; Sais, Lakhdar ; Salhi, Y. ; Tabia, Karim
Author_Institution :
Univ. Lille Nord de France, Lille, France
Abstract :
In this paper, we show how symmetries, a fundamental structural property, can be used to prune the search space in itemset mining problems. Our approach is based on a dynamic integration of symmetries in APRIORI-like algorithms to prune the set of possible candidate patterns. More precisely, for a given itemset, symmetry can be applied to deduce other itemsets while preserving their properties. We also show that our symmetry-based pruning approach can be extended to the general Mannila and Toivonen pattern mining framework. Experimental results highlight the usefulness and the efficiency of our symmetry-based pruning approach.
Keywords :
data mining; integration; search problems; Mannila pattern mining framework; Toivonen pattern mining framework; apriori-like algorithms; candidate patterns pruning; dynamic integration; itemset mining problems; search space pruning; structural property; symmetry-based pruning; Data mining; Heuristic algorithms; Image color analysis; Itemsets; Runtime; itemset mining; symmetry;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
Conference_Location :
Herndon, VA
Print_ISBN :
978-1-4799-2971-9
DOI :
10.1109/ICTAI.2013.78