Implementation of Bio-Inspired Algorithms in High Utility Itemset Mining
Keerthi Mohan1, J. Anitha2, G. Nandini3
1Keerthi Mohan, Dept. Of Computer Science & Engineering, DSATM, Bengaluru, India.
2Dr. J. Anitha, Professor, Dept. Of Computer Science & Engineering, DSATM, Bengaluru, India.
3G. Nandini, Dept. Of Computer Science & Engineering, RRCE, Bengaluru, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP:7238-7243 | Volume-9 Issue-1, October 2019 | Retrieval Number: F9078088619/2019©BEIESP | DOI: 10.35940/ijeat.F9078.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Utility based itemset mining has evolved as an important research topic in data mining, having application in retail-market data analysis, stock market prediction, online advertising and so on. Bio-inspired computation attempts to replicate the way in which biological organisms and sub organisms operate using abstract computing ideas from living phenomena or biological systems. This study focuses on the application of bio-inspired algorithms on high utility itemset mining. A detailed analysis on the performance of the sealgorithms were conducted on various parameters such as execution time, memory usage and the number of high utility items identified. Experimental result suggest Particle Swarm Optimization excels in its efficiency in execution time and memory usage. When the number of high utility items identified are concerned, it is Genetic Algorithm which outperforms Particle Swarm optimization and Bats algorithm.
Keywords: Utility based mining, Bio-inspired algorithms, High utility itemset