COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data. (1st September 2022)

• Record Type: Journal Article
• Authors: Sowmiya, N.; Gupta, N.Srinivasa; Natarajan, Elango; Valarmathi, B; Elamvazuthi, I.; Parasuraman, S.; Kit, Chun Ang; Freitas, Lídio Inácio; Abraham Gnanamuthu, Ezra Morris
• Other Names: Deivanayagampillai Nagarajan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-09-01
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/4414784
• Languages: English
• ISSNs: 1024-123X

Abstract

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.