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Incremental Kernel Fuzzy c-Means
2012The size of everyday data sets is outpacing the capability of computational hardware to analyze these data sets. Social networking and mobile computing alone are producing data sets that are growing by terabytes every day. Because these data often cannot be loaded into a computer’s working memory, most literal algorithms (algorithms that require access
Timothy C. Havens +2 more
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Fuzzy C-Means in High Dimensional Spaces
International Journal of Fuzzy System Applications, 2011High dimensions have a devastating effect on the FCM algorithm and similar algorithms. One effect is that the prototypes run into the centre of gravity of the entire data set. The objective function must have a local minimum in the centre of gravity that causes FCM’s behaviour. In this paper, examine this problem.
Roland Winkler +2 more
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Variable Width Rough-Fuzzy c-Means
2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2017The richness of soft clustering algorithms in the scientific literature reflects from one side the complexity of the underlying problem and from the other the many attempts that have been made to preserve interpretability while modeling vagueness through different theories.
Ferone, Alessio +2 more
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Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters, 2003Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Jiu-Lun Fan 0001 +2 more
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Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering
2015The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers.
P. Gopala Krishna, D. Lalitha Bhaskari
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On efficiency of optimization in fuzzy \(c\)-means
Neural Parallel Sci. Comput., 2002Summary: The efficiency of optimization in fuzzy \(c\)-means clustering is investigated. Numerous, powerful, general-purpose simultaneous optimization methods, and hybrid methods combining these and the most widely used Alternating Optimization (AO) method, are extensively tested for speed comparison.
Yingkang Hu, Richard J. Hathaway
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Local convergence of the fuzzy c-Means algorithms
Pattern Recognition, 1986In this paper we prove a local convergence property, that is, a property pertaining to iteration sequences started near a solution. Specifically, a simple result is proved which shows that whenever an FCM algorithm is started sufficiently near a minimizer of the corresponding objective function, then the iteration sequence must converge to that ...
Richard J. Hathaway, James C. Bezdek
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Optimality tests for the fuzzy c-means algorithm
Pattern Recognition, 1994zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wen Wei, Jerry M. Mendel
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2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019
Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.
Yoosof Mashayekhi +3 more
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Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results.
Yoosof Mashayekhi +3 more
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NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society, 2008
Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present
P. Hore +3 more
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Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present
P. Hore +3 more
openaire +1 more source