A Study of Fuzzy Clustering Ensemble Algorithm Focusing on Medical Data Analysis

2017 ◽  
pp. 383-396 ◽  
Author(s):  
Zhisheng Zhao ◽  
Yang Liu ◽  
Jing Li ◽  
Jiawei Wang ◽  
Xiaozheng Wang
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Medical Data ◽  
Clustering Ensemble ◽  
Ensemble Algorithm ◽  
Fuzzy Clustering Ensemble

Fuzzy Clustering Ensemble Considering Cluster Dependability

2021 ◽  
Author(s):  
Z. Chen ◽  
A. Bagherinia ◽  
B. Minaei-Bidgoli ◽  
H. Parvin ◽  
K.-H. Pho
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Ensemble ◽  
Fuzzy Clustering Ensemble

A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters

2019 ◽  
Vol 49 (7) ◽  
pp. 2567-2581 ◽  
Author(s):  
Musa Mojarad ◽  
Samad Nejatian ◽  
Hamid Parvin ◽  
Majid Mohammadpoor
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Ensemble ◽  
Fuzzy Clustering Ensemble

Elite fuzzy clustering ensemble based on clustering diversity and quality measures

2018 ◽  
Vol 49 (5) ◽  
pp. 1724-1747 ◽  
Author(s):  
Ali Bagherinia ◽  
Behrooz Minaei-Bidgoli ◽  
Mehdi Hossinzadeh ◽  
Hamid Parvin
Keyword(s):  
Fuzzy Clustering ◽  
Quality Measures ◽  
Clustering Ensemble ◽  
Fuzzy Clustering Ensemble

Reliability-based fuzzy clustering ensemble

2020 ◽  
Author(s):  
Ali Bagherinia ◽  
Behrooz Minaei-Bidgoli ◽  
Mehdi Hosseinzadeh ◽  
Hamid Parvin
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Ensemble ◽  
Fuzzy Clustering Ensemble

scholarly journals Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

2021 ◽  
Vol 5 (5) ◽  
pp. 688-699
Author(s):  
Abas Hasanovich Lampezhev ◽  
Elena Yur`evna Linskaya ◽  
Aslan Adal`bievich Tatarkanov ◽  
Islam Alexandrovich Alexandrov
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Equivalence Relation ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Data Uncertainty ◽  
Mathematical Tool ◽  
Cluster Data ◽  
Clustering Quality ◽  
Medical Diagnostic

This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF

scholarly journals Information Theoretic Weighted Fuzzy Clustering Ensemble

2021 ◽  
Vol 67 (1) ◽  
pp. 369-392
Author(s):  
Yixuan Wang ◽  
Liping Yuan ◽  
Harish Garg ◽  
Ali Bagherinia ◽  
Ham飀 Parv飊 ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Ensemble ◽  
Information Theoretic ◽  
Fuzzy Clustering Ensemble
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