relation: https://khub.utp.edu.my/scholars/17532/
title: COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data
creator: Sowmiya, N.
creator: Gupta, N.S.
creator: Natarajan, E.
creator: Valarmathi, B.
creator: Elamvazuthi, I.
creator: Parasuraman, S.
creator: Kit, C.A.
creator: Freitas, L.I.
creator: Abraham Gnanamuthu, E.M.
description: 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. © 2022 N. Sowmiya et al.
publisher: Hindawi Limited
date: 2022
type: Article
type: PeerReviewed
identifier:   Sowmiya, N. and Gupta, N.S. and Natarajan, E. and Valarmathi, B. and Elamvazuthi, I. and Parasuraman, S. and Kit, C.A. and Freitas, L.I. and Abraham Gnanamuthu, E.M.  (2022) COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data.  Mathematical Problems in Engineering, 2022.   ISSN 1024123X     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137931208&doi=10.1155%2f2022%2f4414784&partnerID=40&md5=8dc03686d40bd68e55e6d07b80bcbe26
relation: 10.1155/2022/4414784
identifier: 10.1155/2022/4414784