Image fusion method based on intuitionistic fuzzy clustering

Wang, Zhihui; Tie, Yong; Li, Shuhua

doi:10.1117/12.831064

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2013

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“…Ref. [7] proposed an intuitionistic fuzzy clustering algorithm based distributed clustering method , the algorithm is calculated from two levels : the local level and the global level. Ref.…”

Section: Introductionmentioning

confidence: 99%

A new intuitionistic fuzzy c-means clustering algorithm

Jiang¹,

Zhou²,

Feng³

et al. 2013

Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

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This paper presents a new intuitionistic fuzzy cmeans (IFCM)clustering algorithm by adapting a new method to calculate the hesitation degree of data point in cluster. From the definition of fuzzy entropy, if a clustering result of a data point has bigger fuzzy entropy, the clustering result should have more uncertainty. It means that we have insufficient information to deal with the clustering of a data point, so the hesitation degree of clustering result of the data point should be greater. Form this opinion, a mathematical model is applied to calculate the hesitation degree of clustering of data point based on fuzzy entropy is given. An IFCM clustering algorithms is present.Experiments are performed using two-dimensional synthetic data-sets referred from previous papers. Results have shown that proposed algorithm is not only effective for linear and nonlinear separation, but also able to describe more information comparing to fuzzy c-means clustering algorithm.

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Section: Introductionmentioning

confidence: 99%