Literature DB >> 26557926

Survey on granularity clustering.

Shifei Ding1, Mingjing Du2, Hong Zhu2.   

Abstract

With the rapid development of uncertain artificial intelligent and the arrival of big data era, conventional clustering analysis and granular computing fail to satisfy the requirements of intelligent information processing in this new case. There is the essential relationship between granular computing and clustering analysis, so some researchers try to combine granular computing with clustering analysis. In the idea of granularity, the researchers expand the researches in clustering analysis and look for the best clustering results with the help of the basic theories and methods of granular computing. Granularity clustering method which is proposed and studied has attracted more and more attention. This paper firstly summarizes the background of granularity clustering and the intrinsic connection between granular computing and clustering analysis, and then mainly reviews the research status and various methods of granularity clustering. Finally, we analyze existing problem and propose further research.

Keywords:  Clustering analysis; Granular computing; Granularity clustering

Year:  2015        PMID: 26557926      PMCID: PMC4635389          DOI: 10.1007/s11571-015-9351-3

Source DB:  PubMed          Journal:  Cogn Neurodyn        ISSN: 1871-4080            Impact factor:   5.082


  8 in total

1.  An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering.

Authors:  Witold Pedrycz; Andrzej Bargiela
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2011-11-03

2.  Efficient uncertainty minimization for fuzzy spectral clustering.

Authors:  Brian S White; David Shalloway
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-11-24

3.  Fuzzy-rough supervised attribute clustering algorithm and classification of microarray data.

Authors:  Pradipta Maji
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2010-06-10

4.  Recursive information granulation: aggregation and interpretation issues.

Authors:  A Bargiela; W Pedrycz
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2003

5.  Interpreting concept learning in cognitive informatics and granular computing.

Authors:  Yiyu Yao
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2009-03-31

6.  Spatial clustering property and its self-similarity in membrane potentials of hippocampal CA1 pyramidal neurons for a spatio-temporal input sequence.

Authors:  Yasuhiro Fukushima; Minoru Tsukada; Ichiro Tsuda; Yutaka Yamaguti; Shigeru Kuroda
Journal:  Cogn Neurodyn       Date:  2007-10-12       Impact factor: 5.082

7.  Extending Data Reliability Measure to a Filter Approach for Soft Subspace Clustering.

Authors:  T Boongoen; N Iam-On
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2011-07-28

8.  Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

Authors:  Jian-Hua Zhang; Xiao-Di Peng; Hua Liu; Jörg Raisch; Ru-Bin Wang
Journal:  Cogn Neurodyn       Date:  2013-01-23       Impact factor: 5.082

  8 in total

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