Results 31 to 40 of about 2,884,172 (338)
Automatic generation of fuzzy classification rules using granulation-based adaptive clustering [PDF]
, 2015 A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its
Abbod, MF, Al-Shammaa, M
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Robust constrained fuzzy clustering [PDF]
Information Sciences, 2013 It is well-known that outliers and noisy data can be very harmful when applying clustering methods. Several fuzzy clustering methods which are able to handle the presence of noise have been proposed. In this work, we propose a robust clustering approach called F-TCLUST based on an “impartial” (i.e., self-determined by data) trimming.
Fritz, Heinrich +2 more
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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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Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters [PDF]
Computational Intelligence and Neuroscience, 2013 We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived.
Geweniger, Tina +4 more
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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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Probabilistic clustering algorithms for fuzzy rules decomposition [PDF]
, 2007 The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to
Igrejas, Getúlio, Salgado, Paulo
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A new fuzzy set merging technique using inclusion-based fuzzy clustering [PDF]
, 2008 This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes.
Kaymak, U, Nefti-Meziani, S, Oussalah, M
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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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BigFCM: Fast, Precise and Scalable FCM on Hadoop
, 2016 Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations.
Ghadiri, Nasser +2 more
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