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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Hybrid strategy for selecting compact set of clustering partitions

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Author(s):
Antunes, Vanessa [1] ; Sakata, Tiemi C. [1] ; Faceli, Katti [1] ; de Souto, Marcilio C. P. [2]
Total Authors: 4
Affiliation:
[1] Univ Fed Sao Carlos, Campus Sorocaba Rodovia Joao Leme dos Santos, BR-18052780 Sorocaba, SP - Brazil
[2] Univ Orleans, LIFO EA 4022, Orleans - France
Total Affiliations: 2
Document type: Journal article
Source: APPLIED SOFT COMPUTING; v. 87, FEB 2020.
Web of Science Citations: 0
Abstract

The selection of the most appropriate clustering algorithm is not a straightforward task, given that there is no clustering algorithm capable of determining the actual groups present in any dataset. A potential solution is to use different clustering algorithms to produce a set of partitions (solutions) and then select the best partition produced according to a specified validation measure; these measures are generally biased toward one or more clustering algorithms. Nevertheless, in several real cases, it is important to have more than one solution as the output. To address these problems, we present a hybrid partition selection algorithm, HSS, which accepts as input a set of base partitions potentially generated from clustering algorithms with different biases and aims, to return a reduced and yet diverse set of partitions (solutions). HSS comprises three steps: (i) the application of a multiobjective algorithm to a set of base partitions to generate a Pareto Front (PF) approximation; (ii) the division of the solutions from the PF approximation into a certain number of regions; and (iii) the selection of a solution per region by applying the Adjusted Rand Index. We compare the results of our algorithm with those of another selection strategy, ASA. Furthermore, we test HSS as a post-processing tool for two clustering algorithms based on multiobjective evolutionary computing: MOCK and MOCLE. The experiments revealed the effectiveness of HSS in selecting a reduced number of partitions while maintaining their quality. (AU)

FAPESP's process: 15/21560-0 - Interactive visualization tool for exploring and learning from ensemble of clustering solutions
Grantee:Katti Faceli
Support Opportunities: Scholarships abroad - Research