Authors: Elaine Cristina De Assis; Renata Maria Cardoso Rodrigues De Souza; Getulio José Amorim Do Amaral
Addresses: Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Aníbal Fernandes s/n, 50740-560, Recife, PE, Brazil ' Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Aníbal Fernandes s/n, 50740-560, Recife, PE, Brazil ' Departamento de Estatística, Universidade Federal de Pernambuco, Av. Professor Luiz Freire s/n, 50740-540, Recife, PE, Brazil
Abstract: Partitional clustering algorithms find a partition maximising or minimising some numerical criterion. Statistical shape analysis is used to make decisions observing the shape of objects. The shape of an object is the remaining information when the effects of location, scale and rotation are removed. This paper introduces clustering algorithms suitable for planar shapes. Four numerical criteria are adapted to each algorithm. In order to escape from local optima to reach a better clustering, these algorithms are performed in the framework of bagging procedures. Simulation studies are carried to validate these proposed methods and two real-life datasets are also considered. The experiment quality is assessed by the corrected Rand index and the results the application of the proposed algorithms showed the effectiveness of these algorithms using different clustering criteria and the union of the bagging method to the cluster algorithms provided substantial gains in of the quality of the clusters.
Keywords: statistical shape analysis; partitional clustering methods; bagging procedure.
International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.1, pp.30 - 48
Received: 12 Jun 2017
Accepted: 11 Apr 2018
Published online: 29 Oct 2020 *