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AN APPROACH TO REMOVE THE EFFECT OF RANDOM INITIALIZATION FROM FUZZY C-MEANS CLUSTERING TECHNIQUE [PDF]

Journal of Process Management and New Technologies, 2014
Out of the different available fuzzy clustering techniques Bezdek’s Fuzzy C-Means clustering technique is among the most popular ones. Due to the random initialization of the membership values the performance of Fuzzy C-Means clustering technique ...
Samarjit Das, Hemanta K. Baruah
doaj  

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data [PDF]

Journal of Artificial Intelligence and Data Mining, 2020
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data.
J. Tayyebi, E. Hosseinzadeh
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Extended incremental fuzzy clustering algorithm for sparse high-dimensional big data [PDF]

Jisuanji gongcheng, 2019
Fuzzy C-Means(FCM) clustering algorithm can only deal with low-dimensional data and is sensitive to the initial center,without considering the interactions between class centers.For this reason,an improved method of initial center selection is designed ...
QIAN Xuezhong,YAO Linya
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Combination Evaluation Method of Fuzzy C-Mean Clustering Validity Based on Hybrid Weighted Strategy

IEEE Access, 2021
Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function.
H. Y. Wang, J. S. Wang, G. Wang
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Fuzzy clustering of time series gene expression data with cubic-spline [PDF]

, 2013
Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success.
Ali, Akhtar, Wang, Yu, Angelova, Maia
core   +1 more source

On-line identification of MIMO evolving Takagi-Sugeno fuzzy models [PDF]

, 2004
Evolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori information.
Angelov, Plamen, Xydeas, C, Filev, D
core   +4 more sources

Stock Data Clustering of Food and Beverage Company

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2007
Cluster analysis can be defined as identifying groups of similar objects to discover distribution of patterns and interesting correlations in large data sets.
Shofwatul Uyun, Subanar Subanar
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TOWARDS FINDING A NEW KERNELIZED FUZZY C-MEANS CLUSTERING ALGORITHM [PDF]

Journal of Process Management and New Technologies, 2014
Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead ...
Samarjit Das, Hemanta K. Baruah
doaj  

ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS

Media Statistika, 2015
Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent ...
Baiq Nurul Haqiqi, Robert Kurniawan
doaj   +1 more source

Accelerating fuzzy clustering [PDF]

Information Sciences, 2009
This paper extends earlier work [C. Borgelt, R. Kruse, Speeding up fuzzy clustering with neural network techniques, in: Proceedings of the 12th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'03, St. Louis, MO, USA), IEEE Press, Piscataway, NJ, USA, 2003] on an approach to accelerate fuzzy clustering by transferring methods that were ...
openaire   +2 more sources