Dynamic Type-2 Fuzzy Dependent Dirichlet Regression Mixture clustering model

[Display omitted] •A dynamic Type2 Fuzzy Dependent Dirichlet Regression Mixture clustering model.•It finds optimal numbers of new, transient, and existing clusters dynamically.•It ensures assignment of data to a cluster which has the most similarity to them.•Despite fuzzy clustering models, it suppo...

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Bibliographic Details
Published in:Applied soft computing Vol. 57; pp. 577 - 604
Main Authors: Gamasaee, R., Zarandi, M.H. Fazel
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2017
Subjects:
Dependent Dirichlet Process Mixture
Dynamic regression clustering
Interval Type-2 Fuzzy C-Regression Clustering
Piecewise Regression Mixture
Segmentation
Piecewise Regression Mixture
Segmentation
Dependent Dirichlet Process Mixture
Interval Type-2 Fuzzy C-Regression Clustering
Dynamic regression clustering
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:[Display omitted] •A dynamic Type2 Fuzzy Dependent Dirichlet Regression Mixture clustering model.•It finds optimal numbers of new, transient, and existing clusters dynamically.•It ensures assignment of data to a cluster which has the most similarity to them.•Despite fuzzy clustering models, it supports infinite number of clusters. In this paper, a new dynamic Interval Type-2 Fuzzy Dependent Dirichlet Piecewise Regression Mixture (IT2FDDPRM) clustering model is proposed. The model overcomes shortcomings of both Dependent Dirichlet Process Mixture (DDPM) technique and Interval Type-2 Fuzzy C-regression Clustering Model (IT2FCRM). DDPM method demonstrates that the probability of assigning data to a cluster including the maximum number of data among all clusters is higher, and it ignores the similarity of data to a cluster. However, the new IT2FDDPRM clustering technique supports assignment of data to a cluster which has the most similarity to them. It also allows the model to generate infinite number of clusters. Moreover, it has the capability of segmenting functions assigned to clusters. The model is validated using statistical tests, three validity functions, and mean square error of the model. The results of numerical experiments show that the proposed method has superior performance to other clustering techniques in literature.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2017.04.003