TY - JOUR
T1 - A genetic algorithm coupled with tree-based pruning for mining closed association rules
AU - Poovan, Jashma Suresh Ponmudiyan
AU - Udupi, Dinesh Acharya
AU - Reddy, Nandanavana Veerappareddy Subba
N1 - Publisher Copyright:
© 2023 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2023/6
Y1 - 2023/6
N2 - Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.
AB - Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.
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U2 - 10.11591/ijece.v13i3.pp2876-2890
DO - 10.11591/ijece.v13i3.pp2876-2890
M3 - Article
AN - SCOPUS:85149143908
SN - 2088-8708
VL - 13
SP - 2876
EP - 2890
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 3
ER -