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Publication
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Research Title |
Chaotic Mountain Gazelle Optimizer (CMGO): A RobustOptimization Algorithm for K-Means Clustering of Diverse Data Types |
Date of Distribution |
27 November 2023 |
Conference |
Title of the Conference |
1st International Conference on Data Science & Artificial Intelligence |
Organiser |
Asian Institute of Technology |
Conference Place |
Berkeley Hotel Pratunam, Bangkok, Thailand |
Province/State |
กรุงเทพมหานคร |
Conference Date |
27 November 2023 |
To |
29 November 2023 |
Proceeding Paper |
Volume |
1 |
Issue |
1 |
Page |
18-34 |
Editors/edition/publisher |
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Abstract |
Addressing challenges in data clustering for diverse data types, we introduce
the Chaos Mountain Gazelle Optimizer (CMGO). This enhanced Mountain Gazelle Optimizer (MGO) is tailored for K-means clustering solutions. Noticing a
skew in MGO's strategy distribution, we integrated a chaotic map into the Territorial Solitary Males strategy and omitted the Migration to Search for Food strategy. This adjustment increases exploration and curtails exploitation, improving
CMGO's effectiveness in clustering complex datasets. We implemented the
Gower distance technique to navigate K-means clustering's limitations with categorical and binary data. Tests on numeric, binary, categorical, and mixed data
underscore the clustering's versatility. We evaluated CMGO against 14 algorithms on 28 UCI and OpenML datasets using the F-Measure metric and the tied
rank test for statistical significance ranking. CMGO outperforms the original
MGO and other tested algorithms in clustering pure numeric and categorical data,
securing first place, and third for mixed data. Thus, CMGO emerges as a robust,
efficient K-means optimizing method for complex, diverse datasets. |
Author |
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Peer Review Status |
มีผู้ประเมินอิสระ |
Level of Conference |
นานาชาติ |
Type of Proceeding |
Full paper |
Type of Presentation |
Oral |
Part of thesis |
true |
ใช้สำหรับสำเร็จการศึกษา |
ไม่เป็น |
Presentation awarding |
false |
Attach file |
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Citation |
0
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