Cluster identification and scaling methods based on comparative quantification for dissimilarity data

• Published in: IEEE International Fuzzy Systems conference proceedings pp. 1 - 6
• Main Authors: Sato-Ilic, Mika, Ilic, Peter
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: 3-way dissimilarity data; Analytical models; Data models; Data structures; dissimilarity data; fuzzy cluster; Mathematical model; multidimensional scaling; Object recognition; Principal component analysis; Symmetric matrices
• ISSN: 1558-4739
• DOI: 10.1109/FUZZ-IEEE.2017.8015443

This paper proposes two methods. One is the cluster identification method for 3-way dissimilarity data among objects over times (or subjects) and the other is the cluster scaling method for dissimilarity data among objects. Both methods are based on the comparative quantification model which can obtain the quantitative amount of relationship between a pair of clusters or relationship between a cluster and a basis which spans a subspace constructed a scale. The merits of these methods are that we can obtain "comparability" of obtained clusters over times (or subjects) and supply an "adaptable scale" for observed dissimilarity between a pair of objects, in order to reduce the number of dimensions of the observed data and explain the dissimilarity relationships among objects in the lower dimensional subspace. Numerical examples to investigate the educational effectiveness by using the cognitive 3-way dissimilarity data of students demonstrate a better performance for the proposed methods.