Results 291 to 300 of about 2,884,172 (338)

Fuzzy clustering

2018
The objective of this thesis is to represent an alternative clustering technique which is among the most famous, called as Fuzzy c-Means algorithm. Fuzzy clustering methods, like Fuzzy c-Means and Gustafson Kessel extension, allow the objects to belong to several clusters simultaneously with different degrees of membership.
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Increasing energy efficiency of rule-based fuzzy clustering algorithms using CLONALG-M for wireless sensor networks

Applied Soft Computing, 2021
S. A. Sert, A. Yazıcı
semanticscholar   +1 more source

Fuzziness indices for fuzzy clustering

The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004
Some indices of fuzziness are introduced for providing helpful information in fuzzy clustering. These indices play an auxiliary role in fuzzy clustering and can be used for deciding the number of clusters by combining with another criterion. Numerical examples are given for demonstrating how these indices can be applied.
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Fuzzy elastic clustering

[Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 2002
The authors introduce a new hybrid method called fuzzy elastic clustering for clustering and classification of patterns. It generates closed loops around a set of training patterns and sections off portions of the hyperspace thus enclosing clusters of patterns.
R. Srikanth, F.E. Petry, C. Koutsougeras
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Deep FC-IIWO Fuzzy Clustering: An Optimized Fuzzy Clustering Approach for Big Data Clustering

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A significant role is played by clustering approaches in data mining processes, which becomes more challenging because of the rising dimension of the available databases. Clustering strategies are employed in various sectors like information retrieval, social network analytics, image processing, and so on.
Sudha, D., Gowri, S.
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Agglomerative Fuzzy Clustering

2016
The term fuzzy clustering usually refers to prototype-based methods that optimize an objective function in order to find a (fuzzy) partition of a given data set and are inspired by the classical c-means clustering algorithm. Possible transfers of other classical approaches, particularly hierarchical agglomerative clustering, received much less ...
Christian Borgelt, Rudolf Kruse
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Fuzzy Clustering of Ecological Data

1991
Ordination and classification have always been important stages in ecological data analysis. This paper presents a clustering technique based on fuzzy sets to obtain both ordination and classification particularly well suited for ecological analyses.
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Collaborative fuzzy clustering

Pattern Recognition Letters, 2002
Summary: We introduce a new clustering architecture in which several subsets of patterns can be processed together with an objective of finding a structure that is common to all of them. To reveal this structure, the clustering algorithms operating on the separate subsets of data collaborate by exchanging information about local partition matrices.
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Evolutionary fuzzy clustering

Proceedings of 1995 IEEE International Conference on Evolutionary Computation, 2002
Genetic algorithms and evolutionary programming methods are employed to perform fuzzy clustering. The experimental results are compared favourably against that of the fuzzy c-means algorithm, and their theoretical justification is given.
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FCM: The fuzzy c-means clustering algorithm

, 1984
J. Bezdek, R. Ehrlich, W. Full
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