Boosted-oriented probabilistic smoothing-spline clustering of series

Carmela Iorio (), Gianluca Frasso (), Antonio D’Ambrosio () and Roberta Siciliano ()
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Carmela Iorio: University of Naples Federico II
Gianluca Frasso: Wageningen University and Research
Antonio D’Ambrosio: University of Naples Federico II
Roberta Siciliano: University of Naples Federico II

Statistical Methods & Applications, 2023, vol. 32, issue 4, No 4, 1123-1140

Abstract: Abstract Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. However, a factor that influences the performance of fuzzy algorithms is the value of fuzzifier parameter. In this paper, we propose a fuzzy clustering procedure for data (time) series that does not depend on the definition of a fuzzifier parameter. It comes from two approaches, theoretically motivated for unsupervised and supervised classification cases, respectively. The first is the Probabilistic Distance clustering procedure. The second is the well known Boosting philosophy. Our idea is to adopt a boosting prospective for unsupervised learning problems, in particular we face with non hierarchical clustering problems. The global performance of the proposed method is investigated by various experiments.

Keywords: Boosting; Fuzzy clustering; Probabilistic distance clustering; Cluster validity (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-022-00665-y

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