Results 11 to 20 of about 22,177,297 (251)
On the selection of m for Fuzzy c-Means [PDF]
Advances in Intelligent Systems Research, 2015 Fuzzy c-means is a well known fuzzy clustering algorithm. It is an unsupervised clustering algorithm that permits us to build a fuzzy partition from data. The algorithm depends on a parameter m which corresponds to the degree of fuzziness of the solution. Large values of m will blur the classes and all elements tend to belong to all clusters.
Vicenç Torra, Torra, Vicenç,
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Automatic Genetic Fuzzy c-Means
Journal of Intelligent Systems, 2018 Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers.
Jebari Khalid +2 more
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A Bayesian Interpretation of Fuzzy C-Means
, 2023 In Explainable Artificial Intelligence, the interpretation of the decisions provided by a model is of primary importance. In this context, we consider Fuzzy C-Means (FCM), which is a clustering algorithm that induces a model from data by assigning, to ...
Corrado Mencar, Ciro Castiello
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Fuzzy c-means with variable compactness [PDF]
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008 Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of ...
Snehashis Roy +5 more
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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
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, 2020 A simple python implementation of Fuzzy C-means ...
dirk, Madson Dias, Alberth Florêncio
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International Journal on Advanced Science, Engineering and Information Technology, 2019 Among the inherited blood disorders in Southeast Asia, thalassemia is the most prevalent. Thalassemias are pathologies that derive from genetic defects of the globin genes. Thalassemia is also considered a health burden among the world’s population. Thalassemia cannot be cured, but there is a method to prevent the occurrence of thalassemia by early ...
Zuherman Rustam +4 more
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Applying the possibilistic C-means algorithm in kernel-induced spaces [PDF]
, 2010 In this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the
Masulli, F. +5 more
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Performance comparison of fuzzy and non-fuzzy classification methods
Egyptian Informatics Journal, 2016 In data clustering, partition based clustering algorithms are widely used clustering algorithms. Among various partition algorithms, fuzzy algorithms, Fuzzy c-Means (FCM), Gustafson–Kessel (GK) and non-fuzzy algorithm, k-means (KM) are most popular ...
B. Simhachalam, G. Ganesan
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Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera [PDF]
, 2015 Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation.
Longstaff, Andrew P. +4 more
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