 COIN: Correlation Index-Based Similarity Measure for Clustering Categorical DataN. Sowmiya, N.Srinivasa Gupta, Elango Natarajan, B Valarmathi, I. Elamvazuthi, S. Parasuraman, Chun Ang Kit, LÃdio Inácio Freitas, Ezra Morris Abraham Gnanamuthu and Nagarajan Deivanayagampillai Mathematical Problems in Engineering, 2022, vol. 2022, 1-12 Abstract: In this paper, a correlation index-based clustering algorithm (COIN) is proposed for clustering the categorical data. The proposed algorithm was tested on nine datasets gathered from the University of California at Irvine (UCI) repository. The experiments were made in two ways, one by specifying the number of clusters and another without specifying the number of clusters. The proposed COIN algorithm is compared with five existing categorical clustering algorithms such as Mean Gain Ratio (MGR), Min–Min-Roughness (MMR), COOLCAT, K-ANMI, and G-ANMI. The result analysis clearly reports that COIN outperforms other algorithms. It produced better accuracies for eight datasets (88.89%) and slightly lower accuracy for one dataset (11%) when compared individually with MMR, K-ANMI, and MGR algorithms. It produced better accuracies for all nine datasets (100%) when it is compared with G-ANMI and COOLCAT algorithms. When COIN was executed without specifying the number of clusters, it outperformed MGR for 88.89% of the test instances and produced lower accuracy for 11% of the test instances. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4414784 DOI: 10.1155/2022/4414784 Access Statistics for this article More articles in Mathematical Problems in Engineering from Hindawi |
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