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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
Summary
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.
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.
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