Positional and confidence voting-based consensus functions for fuzzy cluster ensembles

2012 ◽  
Vol 193 ◽  
pp. 1-32 ◽  
Author(s):  
Xavier Sevillano ◽  
Francesc Alías ◽  
Joan Claudi Socoró
Keyword(s):  
Fuzzy Cluster ◽  
Cluster Ensembles ◽  
Consensus Functions

OPTIMIZING FUZZY CLUSTER ENSEMBLE IN STRING REPRESENTATION

2013 ◽  
Vol 27 (02) ◽  
pp. 1350005 ◽  
Author(s):  
HOSEIN ALIZADEH ◽  
BEHROUZ MINAEI-BIDGOLI ◽  
HAMID PARVIN
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Search Space ◽  
Fuzzy Cluster ◽  
Suggested Model ◽  
Cluster Ensembles ◽  
Mutation Operators ◽  
Candidate Solution ◽  
Nonlinear Objective Function ◽  
Crossover And Mutation

In this paper, we present a novel optimization-based method for the combination of cluster ensembles. The information among the ensemble is formulated in 0-1 bit strings. The suggested model defines a constrained nonlinear objective function, called fuzzy string objective function (FSOF), which maximizes the agreement between the ensemble members and minimizes the disagreement simultaneously. Despite the crisp primary partitions, the suggested model employs fuzzy logic in the mentioned objective function. Each row in a candidate solution of the model includes membership degrees indicating how much data point belongs to each cluster. The defined nonlinear model can be solved by every nonlinear optimizer; however; we used genetic algorithm to solve it. Accordingly, three suitable crossover and mutation operators satisfying the constraints of the problem are devised. The proposed crossover operators exchange information between two clusters. They use a novel relabeling method to find corresponding clusters between two partitions. The algorithm is applied on multiple standard datasets. The obtained results show that the modified genetic algorithm operators are desirable in exploration and exploitation of the big search space.

CONSENSUS FUNCTIONS FOR CLUSTER ENSEMBLES

2012 ◽  
Vol 26 (6) ◽  
pp. 598-614 ◽  
Author(s):  
Ghaith Manita ◽  
Riadh Khanchel ◽  
Mohamed Limam
Keyword(s):  
Cluster Ensembles ◽  
Consensus Functions

scholarly journals Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey

2018 ◽  
Vol 8 (4) ◽  
pp. 2351
Author(s):  
Yalamarthi Leela Sandhya Rani ◽  
V. Sucharita ◽  
K. V. V. Satyanarayana
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Ensemble Methods ◽  
Clustering Ensemble ◽  
Cluster Ensemble ◽  
Cluster Ensembles ◽  
Useful Knowledge ◽  
Extensive Analysis ◽  
Consensus Functions

<p class="PreformattedText">Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.</p>

Analysis of Fuzzy Cluster for Mental Health

2013 ◽  
Author(s):  
Kensei Tsuchida ◽  
Chieko Kato ◽  
Tadaaki Kirishima ◽  
Futoshi Sugimoto
Keyword(s):  
Mental Health ◽  
Fuzzy Cluster

A fuzzy cluster method for turbulent/non-turbulent interface detection

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Khaled Younes ◽  
Bradley Gibeau ◽  
Sina Ghaemi ◽  
Jean-Pierre Hickey
Keyword(s):  
Fuzzy Cluster ◽  
Cluster Method ◽  
Fuzzy Cluster Method
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