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Discovery of Interesting Itemsets for Web Service Composition Using Hybrid Genetic Algorithm

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Abstract

Extraction of interesting patterns from large databases have placed great impact on many applications for instance market basket analysis, web service mining, bio-informatics and mobile commerce for effective decision making. One of the methods to unearth the interesting patterns from the databases is High Utility Itemset Mining (HUIM). Research in mining High Utility Itemsets (HUIs) is emerged because frequent itemset mining only focuses on statistical correlations between the items in an itemset. HUIM aims to extract relations between the items in an itemset based on the semantic significance. This article presents a Hybrid Genetic Algorithm (HGA) which combines quantum operators with classical genetic operators to mine interesting patterns from large databases for web service composition. The extensive experimental result show that proposed algorithm is sensible and admissible in terms of running time and memory consumption for extracting high utility itemsets for web service composition.

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  1. Department of Information Technology, Karpagam College of Engineering, Coimbatore, India

    S. Kannimuthu & D. Gowtham Chakravarthy

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  1. S. Kannimuthu
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Kannimuthu, S., Chakravarthy, D.G. Discovery of Interesting Itemsets for Web Service Composition Using Hybrid Genetic Algorithm. Neural Process Lett 54, 3913–3939 (2022). https://doi.org/10.1007/s11063-022-10793-x

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