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Stellar mass black hole optimisation for utility mining

Author

Listed:
  • Subramanian Kannimuthu
  • Kandhasamy Premalatha

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.

Suggested Citation

  • Subramanian Kannimuthu & Kandhasamy Premalatha, 2019. "Stellar mass black hole optimisation for utility mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 11(3), pages 222-245.
    • Handle: RePEc:ids:injdan:v:11:y:2019:i:3:p:222-245
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