Paper Info
Title
High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm.
Abstract
In data mining, mining high utility itemset (HUI) is one among the recent thrust area that receives several approaches for solving it in an effective manner. In the past decade, addressing optimization problems using evolutionary algorithms are an unavoidable strategy due to its convergence towards optimal solution within the stipulated time. The results of evolutionary algorithms on various optimization problems are far effective when compared to the exhaustive approaches with respect to computational time. The problem with HUI is discovering a set of items from a transactional database that possess high level of utility when compared with other distinctive sets. This problem becomes harder while addressing the count of items in the database while its higher and computational time to solve this problem using exhaustive search becomes exponential as proposition of items in transaction database increases. In this paper, an optimization model based on the biological behaviour of grey wolf is proposed; the model namely grey wolf optimization algorithm is used to solve HUI using five different Boolean operations. The proposed model is evaluated using standard performance metrics over synthetic datasets and real-world datasets. The proposed model results are then compared with recent HUIM models to show the significance.
Year
DOI
Venue
2020
10.1007/s00500-020-05123-z
SOFT COMPUTING
Keywords
DocType
Volume
High utility itemset,Boolean operators,Grey wolf optimization algorithm
Journal
24.0
Issue
ISSN
Citations 
21.0
1432-7643
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
N. Pazhaniraja120.70
S. Sountharrajan210.35
B. Sathis Kumar310.35