Collaborative fuzzy clustering algorithm: Some refinements

Abstract Since the inception of the concept of collaborative fuzzy clustering (CFC), many related ideas and algorithms have been proposed. In this study, we offer a synthetic view of this body of knowledge. We further concentrate on the horizontal version of the CFC algorithm being regarded as one of the major branches of the CFC. Our intent is to address the following three open questions: (a) assessing the necessity of reordering partition matrices prior to invoking the collaboration process; (b) analyzing the impact of linkage strengths on the performance of the clustering results; and (c) forming a representative global data structure with the use of the concept of information granules leading to so-called granular partition matrices. A collection of experimental studies is provided to quantify the underlying concepts and algorithms.

[1]  C. L. Philip Chen,et al.  A Collaborative Fuzzy Clustering Algorithm in Distributed Network Environments , 2014, IEEE Transactions on Fuzzy Systems.

[2]  Younès Bennani,et al.  Vertical collaborative clustering using generative topographic maps , 2015, 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[3]  Witold Pedrycz,et al.  Collaborative clustering with the use of Fuzzy C-Means and its quantification , 2008, Fuzzy Sets Syst..

[4]  Chin-Teng Lin,et al.  Vertical Collaborative Fuzzy C-Means for Multiple EEG Data Sets , 2013, ICIRA.

[5]  Chin-Teng Lin,et al.  A preprocessed induced partition matrix based collaborative fuzzy clustering for data analysis , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[6]  Wei Wang,et al.  Granular-computing based hybrid collaborative fuzzy clustering for long-term prediction of multiple gas holders levels , 2016, Inf. Sci..

[7]  Witold Pedrycz,et al.  Collaborative fuzzy clustering , 2002, Pattern Recognit. Lett..

[8]  Chin-Teng Lin,et al.  Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers , 2014, 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA).

[9]  Yunming Ye,et al.  Mining from distributed and abstracted data , 2016, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[10]  Witold Pedrycz,et al.  Building the fundamentals of granular computing: A principle of justifiable granularity , 2013, Appl. Soft Comput..

[11]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[12]  Jyh-Yeong Chang,et al.  Designing mamdani type fuzzy rule using a collaborative FCM scheme , 2013, 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY).

[13]  Pengjiang Qian,et al.  Collaborative Fuzzy Clustering From Multiple Weighted Views , 2015, IEEE Transactions on Cybernetics.

[14]  Witold Pedrycz,et al.  A Multifaceted Perspective at Data Analysis: A Study in Collaborative Intelligent Agents , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Guillaume Cleuziou,et al.  CoFKM: A Centralized Method for Multiple-View Clustering , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[16]  Yves Lechevallier,et al.  A multi-view relational fuzzy c-medoid vectors clustering algorithm , 2015, Neurocomputing.

[17]  Witold Pedrycz,et al.  Semantic Web Content Analysis: A Study in Proximity-Based Collaborative Clustering , 2007, IEEE Transactions on Fuzzy Systems.

[18]  Younès Bennani,et al.  Collaborative Clustering: How to Select the Optimal Collaborators? , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[19]  Chin-Teng Lin,et al.  A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism , 2015, Neurocomputing.

[20]  Witold Pedrycz,et al.  A Multifaceted Perspective at Data Analysis: A Study in Collaborative Intelligent Agents $^{\ast}$ , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Gwanggil Jeon,et al.  Learning Collaboration Links in a Collaborative Fuzzy Clustering Environment , 2007, MICAI.

[22]  Chin-Teng Lin,et al.  Takagi-Sugeno-Kang type collaborative fuzzy rule based system , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[23]  Yves Lechevallier,et al.  Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices , 2013, Fuzzy Sets Syst..

[24]  Witold Pedrycz,et al.  Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines , 2012, IEEE Transactions on Fuzzy Systems.

[25]  Younès Bennani,et al.  Collaborative Clustering using Prototype-Based Techniques , 2012, Int. J. Comput. Intell. Appl..

[26]  Mohammad Hossein Fazel Zarandi,et al.  Relative entropy collaborative fuzzy clustering method , 2015, Pattern Recognit..

[27]  Koen Vanhoof,et al.  Towards a Suitable Reconciliation of the Findings in Collaborative Fuzzy Clustering , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.