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useR! 2024
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8 - 11 July, 2024
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Wednesday, July 10 • 13:30 - 15:00
A New Correlation-Based Fuzzy Cluster Validity Index with UniversalCVI R Package - Onthada Preedasawakul, King Mongkut’s University of Technology Thonburi & Nathakhun Wiroonsri, King Mongkut's University of Technology Thonburi

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The optimal number of clusters is one of the main concerns when applying cluster analysis. Several cluster validity indexes (CVI) have been introduced to address this problem. However, in some situations, there is more than one option that can be chosen as the final number of clusters. In this study, we introduce a fuzzy CVI known as the Wiroonsri–Preedasawakul (WP) index. This index is defined based on the correlation between the actual distance between a pair of data points and the distance between adjusted centroids with respect to that pair. Overall, the WP index outperforms most of the traditional indexes in terms of efficiency and detecting secondary options. Moreover, our index remains effective even when the fuzziness parameter m is set to a large value. Our R package called UniversalCVI used in this work is available at https://CRAN.R-project.org/package=UniversalCVI.
Speakers
avatar for Nathakhun Wiroonsri

Nathakhun Wiroonsri

Assistant Professor, King Mongkut's University of Technology Thonburi
Nathakhun Wiroonsri earned his B.Sc. in Mathematics with first-class honors from Chulalongkorn University, Master of Financial Mathematics from North Carolina State University, and Ph.D. in Applied Mathematics from the University of Southern California in 2010, 2013, and 2018, respectively... Read More →
avatar for Onthada Preedasawakul

Onthada Preedasawakul

Miss Onthada Preedasawakul, King Mongkut’s University of Technology Thonburi
Onthada Preedasawakul is currently pursuing her B.A. in Statistics from the Department of Mathematics at the Faculty of Science in King Mongkut’s University of Technology Thonburi, Bangkok, Thailand. She is a student member of the Mathematics and Statistics with Applications (MaSA... Read More →

Wednesday July 10, 2024 13:30 - 15:00 CEST
TBD
  Machine learning and AI, Poster Session
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