Title: Stellar mass black hole optimisation for utility mining

Authors: Subramanian Kannimuthu; Kandhasamy Premalatha

Addresses: Karpagam College of Engineering, Coimbatore, Tamilnadu, India ' Bannari Amman Institute of Technology, Erode, Tamilnadu, India

Abstract: Major challenges in mining high utility itemsets from the transaction databases requires exponential search space and database-dependent minimum utility threshold. The search space is very large because of the large number of distinct items and size of the database. Data analysts need to specifying appropriate minimum utility thresholds for their data mining tasks though they may have no knowledge pertaining to their databases. To get rid of these problems, Stellar mass black hole optimisation (SBO) method is proposed to mine Top-K HUIs from the transaction database without specifying minimum utility threshold. To know the performance of SBO, the experiment results are compared with GA.

Keywords: data mining; genetic algorithm; stellar mass black hole optimisation; SBO; high utility itemsets; utility mining.

DOI: 10.1504/IJDATS.2019.101155

International Journal of Data Analysis Techniques and Strategies, 2019 Vol.11 No.3, pp.222 - 245

Received: 17 May 2017
Accepted: 11 Aug 2017
Published online: 26 Jul 2019 *

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