TY  - JOUR
Y1  - 2022///
SN  - 1024123X
PB  - Hindawi Limited
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137931208&doi=10.1155%2f2022%2f4414784&partnerID=40&md5=8dc03686d40bd68e55e6d07b80bcbe26
A1  - Sowmiya, N.
A1  - Gupta, N.S.
A1  - Natarajan, E.
A1  - Valarmathi, B.
A1  - Elamvazuthi, I.
A1  - Parasuraman, S.
A1  - Kit, C.A.
A1  - Freitas, L.I.
A1  - Abraham Gnanamuthu, E.M.
JF  - Mathematical Problems in Engineering
VL  - 2022
AV  - none
N1  - cited By 0
N2  - 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.
KW  - Based clustering; Categorical data; Clusterings; Correlation index; Gain Ratio; Mean gain; Number of clusters; Similarity measure; Test instances; University of California
KW  -  Clustering algorithms
TI  - COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data
ID  - scholars17532
ER  -