scholarly journals A Fuzzy Clustering Model for Fuzzy Data with Outliers

2011 ◽  
Vol 1 (2) ◽  
pp. 29-42 ◽  
Author(s):  
M. H. Fazel Zarandi ◽  
Zahra S. Razaee
Keyword(s):  
Fuzzy Clustering ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Wasserstein Distance ◽  
Fuzzy Data ◽  
Simulation Experiments ◽  
Clustering Model ◽  
Interval Valued

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.

scholarly journals A Fuzzy Clustering Model for Fuzzy Data with Outliers

2013 ◽  
pp. 29-40
Author(s):  
M. H. Fazel Zarandi ◽  
Zahra S. Razaee
Keyword(s):  
Fuzzy Clustering ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Wasserstein Distance ◽  
Fuzzy Data ◽  
Simulation Experiments ◽  
Clustering Model ◽  
Interval Valued

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.

Grid Resources’ Fuzzy Clustering Based on Mobile Agent

2011 ◽  
Vol 467-469 ◽  
pp. 1038-1043
Author(s):  
Dong Guo ◽  
Qiang Li ◽  
Meng Zhang ◽  
Bing Xin Guo ◽  
Liang Hu
Keyword(s):  
Fuzzy Clustering ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Grid Resource ◽  
Simulation Experiments ◽  
Clustering Model ◽  
Fuzzy Clustering Method ◽  
Speed Up ◽  
Grid Nodes ◽  
Grid Resources

To speed up grid resources’ clustering, this paper presents a grid resource fuzzy clustering model based on mobile agent. A fuzzy clustering task is decomposed into some parallel subtasks which then are distributed to some grid nodes for parallel processing by using mobile agents through B-shift algorithm so as to improve clustering efficiency. This paper implements grid resources’ fuzzy clustering based on mobile agent with Aglet platform, and evaluates the performances through simulation experiments. The experiments show that the clustering time of this method is shorter than that of center fuzzy clustering method.

Fuzzy clustering algorithm for fuzzy data based on α-cuts

2013 ◽  
Vol 24 (3) ◽  
pp. 511-519 ◽  
Author(s):  
S. Effati ◽  
H. Sadoghi Yazdi ◽  
A. Jiryani Sharahi
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Data ◽  
Fuzzy Clustering Algorithm

Identifying the Mastery Concepts in Linear Algebra by Using FCM-CM Algorithm

2010 ◽  
Vol 44-47 ◽  
pp. 3897-3901
Author(s):  
Hsiang Chuan Liu ◽  
Yen Kuei Yu ◽  
Jeng Ming Yih ◽  
Chin Chun Chen
Keyword(s):  
Objective Function ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Distance Function ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Euclidean Distance Function ◽  
Fuzzy C Means Algorithm

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters by employing Mahalanobis distance in objective function, however, both of them need to add some constrains for Mahalanobis distance. In this paper, the authors’ improved Fuzzy C-Means algorithm based on common Mahalanobis distance (FCM-CM) is used to identify the mastery concepts in linear algebra, for comparing the performances with other four partition algorithms; FCM-M, GG, GK, and FCM. The result shows that FCM-CM has better performance than others.

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