FINDCLUS: fuzzy individual differences clustering. ADditive CLUStering (ADCLUS) is a tool for overlapping clustering of two-way proximity matrices (objects objects). In Simple Additive Fuzzy Clustering (SAFC), a variant of ADCLUS is introduced providing a fuzzy partition of the objects, that is the objects belong to the clusters with the so-called membership degrees ranging from zero (complete non-membership) to one (complete membership). INDCLUS (INdividual Differences CLUStering) is a generalization of ADCLUS for handling three-way proximity arrays (objects objects subjects). Here, we propose a fuzzified alternative to INDCLUS capable to offer a fuzzy partition of the objects by generalizing in a three-way context the idea behind SAFC. This new model is called Fuzzy INdividual Differences CLUStering (FINDCLUS). An algorithm is provided for fitting the FINDCLUS model to the data. Finally, the results of a simulation experiment and some applications to synthetic and real data are discussed.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Bocci, Laura; Vicari, Donatella: ROOTCLUS: searching for “ROOT clusters” in three-way proximity data (2019)
- Bocci, Laura; Vicari, Donatella: GINDCLUS: generalized INDCLUS with external information (2017)
- Giordani, Paolo; Kiers, Henk A. L.: FINDCLUS: fuzzy individual differences clustering (2012)