Results 261 to 270 of about 112,252 (298)

Fuzzy C-means and fuzzy swarm for fuzzy clustering problem

Expert Systems with Applications, 2011
Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement.
Hesam Izakian, Ajith Abraham
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Interval Type-2 Relative Entropy Fuzzy C-Means clustering

Information Sciences, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zarinbal, M., Zarandi, Mohammad Hossein Fazel, Türkşen, İsmail Burhan   +2 more
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Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986
This paper reports the results of a numerical comparison of two versions of the fuzzy c-means (FCM) clustering algorithms. In particular, we propose and exemplify an approximate fuzzy c-means (AFCM) implementation based upon replacing the necessary ``exact'' variates in the FCM equation with integer-valued or real-valued estimates.
Cannon, Robert L., Dave, Jitendra V., Bezdek, James C.   +2 more
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Parallel Fuzzy c-Means Cluster Analysis

2007
This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters' centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for the data, by using the PBM validity index to evaluate the ...
Marta V. Modenesi, Myrian C. A. Costa, Alexandre G. Evsukoff, Nelson F. F. Ebecken   +3 more
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Online Classifiers Based on Fuzzy C-means Clustering

2013
In the online approach a classifier is, as usual, induced from the available training set. However, in addition, there is also some adaptation mechanism providing for a classifier evolution after the classification task has been initiated and started. In this paper two algorithms for online learning and classification are considered.
Joanna Jędrzejowicz, Piotr Jędrzejowicz   +1 more
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Clustering System Group Customers through Fuzzy C-Means Clustering

2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2018
Like other economic sectors, it is important to identify, satisfy, and attract profitable customers in the software industry. Organizations have decided to analyze customer behavior and keep the most valuable customers satisfied due to competitive conditions and customer attraction costs.
Yaser Hasanpour, Shima Nemati, Reza Tavoli   +2 more
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Classification via Deep Fuzzy c-Means Clustering

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
While deep learning has proven to be a powerful new tool for modeling and predicting a wide variety of complex phenomena, those models remain incomprehensible black boxes. This is a critical impediment to the widespread deployment of deep learning technology, as decades of research have found that users simply will not trust (i.e.
Mojtaba Yeganejou, Scott Dick
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Possibilistic c-means clustering using fuzzy relations

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
The aim of this paper is designing a new approach for objective function- based fuzzy clustering. A new algorithm will be proposed for possibilistic c-means (PCM)-based models. This PCM-based algorithm uses fuzzy relations. In order to consider both separation between clusters and compactness within clusters, fuzzy relations will be applied.
M. H. Fazel Zarandi, M. Rostam Niakan Kalhori, M. F. Jahromi   +2 more
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FRCM: A fuzzy rough c-means clustering method

Fuzzy Sets and Systems
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bin Yu, Zijian Zheng, Mingjie Cai, Witold Pedrycz, Weiping Ding   +4 more
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Fuzzy approaches to hard c-means clustering

2012 IEEE International Conference on Fuzzy Systems, 2012
A popular clustering model is hard c-means (HCM). For many data sets the HCM objective function has local extrema, so HCM optimization often yields suboptimal clusterings. The effect of local extrema can be reduced by fuzzification, leading to the well-known fuzzy c-means (FCM) model with the fuzziness parameter m > 1.
Thomas A. Runkler, James M. Keller
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