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New fuzzy C-means clustering method based on feature-weight and cluster-weight learning
Applied Soft Computing, 2019Among fuzzy clustering methods, fuzzy c-means (FCM) is the most recognized algorithm. In this algorithm, it is assumed that all the features are of equal importance.
Mahdi Hashemzadeh +2 more
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Online Classifiers Based on Fuzzy C-means Clustering
2013In 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 +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), 2018Like 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 +2 more
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Classification via Deep Fuzzy c-Means Clustering
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018While 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), 2013The 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 +2 more
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Fuzzy approaches to hard c-means clustering
2012 IEEE International Conference on Fuzzy Systems, 2012A 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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Video summarization using fuzzy c-means clustering
20th Iranian Conference on Electrical Engineering (ICEE2012), 2012The rapid growth of digital world and computer networking are contributing to an enormous and continuous growing of video content. Despite the greatly growth in digital video technologies, the capabilities of users to manipulate, interact with and manage videos are still far behind what users can achieve with other types of media such as text or images.
Ebrahim Asadi +1 more
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A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering
Journal on spesial topics in mobile networks and applications, 2020Manoj Kumar +4 more
semanticscholar +1 more source
Collaborative feature-weighted multi-view fuzzy c-means clustering
Pattern Recognition, 2021Miin-Shen Yang, Kristina P. Sinaga
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