Results 31 to 40 of about 867,994 (193)
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
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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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Evolving single- and multi-model fuzzy classifiers with FLEXFIS-class [PDF]
, 2007 [2] R. Santos, E. Dougherty, and J. A. Jaakko, “Creating fuzzy rules for image classification using biased data clustering,” in SPIE proceedings series (SPIE proc. ser.) International Society for Optical Engineering proceedings series.
Angelov, Plamen +2 more
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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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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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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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Fuzzy Cluster Analysis: Pseudometrics and Fuzzy Clusters
Cybernetics and Computer Technologies, 2023 Introduction. Clustering problems arise in various spheres of human activity. In cases where there are no initial data sufficient for statistical analysis or information obtained from experts is used, fuzzy models are proposed that take into account different types of uncertainty and more argumentatively reflect real situations that model systems of ...
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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
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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
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Chemical Engineering Transactions, 2017 In this paper, the author research on electrical equipment’s fault diagnosis based on the improved support vector machine and fuzzy clustering. Combining the support vector combined fuzzy sets and neural network to carry on the fault diagnosis is a most ...
Yuling Yan
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