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Solving text clustering problem using a memetic differential evolution algorithm.

Hossam M J Mustafa1, Masri Ayob1, Dheeb Albashish2

  • 1Data Mining and Optimization Research Group, Center of Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, Bangi, Malaysia.

Plos One
|June 12, 2020
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Summary
This summary is machine-generated.

A new Memetic Differential Evolution (MDETC) algorithm enhances text clustering by balancing exploration and exploitation. MDETC significantly outperforms existing methods on benchmark datasets, improving document analysis.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Text clustering is crucial for analyzing large volumes of data.
  • Existing algorithms struggle with robustness and balancing exploration/exploitation.
  • Many benchmark datasets reveal limitations in current text clustering approaches.

Purpose of the Study:

  • To propose a novel Memetic Differential Evolution (MDETC) algorithm for text clustering.
  • To improve the balance between exploration and exploitation in clustering.
  • To enhance the overall quality and performance of text document analysis.

Main Methods:

  • Hybridizing differential evolution (DE) mutation with a memetic algorithm (MA).
  • Developing the Memetic Differential Evolution (MDETC) algorithm.
  • Evaluating performance on six standard text clustering benchmark datasets.

Main Results:

  • MDETC demonstrated superior performance compared to other clustering algorithms.
  • The algorithm achieved high scores based on AUC metric and F-measure.
  • Statistical analysis confirmed MDETC's effectiveness and robustness.

Conclusions:

  • The proposed MDETC algorithm offers an effective solution for text clustering.
  • Hybridization enhances exploration and exploitation capabilities for better results.
  • MDETC represents a significant advancement in text document analysis.