A class of two-mode clustering algorithms in a fuzzy setting

Maria Brigida Ferraro, Paolo Giordani and Maurizio Vichi

Econometrics and Statistics, 2021, vol. 18, issue C, 63-78

Abstract: Two-mode clustering consists in simultaneously partitioning modes (e.g., objects and variables) of an observed two-mode data matrix. A class of two-mode clustering algorithms in a fuzzy framework is proposed. Starting from the Double k-Means algorithm, different fuzzy proposals are addressed. The first one is the Fuzzy Double k-Means (FDkM) algorithm, providing two fuzzy partitions, one for each mode. A second proposal is the Fuzzy Double k-Means with polynomial fuzzifiers (FDkMpf) algorithm, a general method that includes the FDkM one as a particular case. Finally, a robust extension is introduced and analyzed by using the concept of noise cluster. The adequacy of the proposed algorithms is checked by means of a simulation and two real-case studies.

Keywords: Two-mode clustering; Fuzzy clustering; Polynomial fuzzifiers; Noise cluster (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:18:y:2021:i:c:p:63-78

DOI: 10.1016/j.ecosta.2020.03.006

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