Results 21 to 30 of about 926,739 (292)
Clustering stock price volatility using intuitionistic fuzzy sets [PDF]
, 2022 Clustering involves gathering a collection of objects into homogeneous groups or clusters, such that objects in the same cluster are more similar when compared to objects present in other groups.
Chountas, P., Urumov, G.
core +1 more source
Real-Time joint Landmark Recognition and Classifier Generation by an Evolving Fuzzy System. [PDF]
, 2006 A new approach to real-time joint classification and classifier design is proposed in this paper. It is based on the recently developed evolving fuzzy system (EFS) method and is applied to mobile robotics.
Angelov, Plamen +3 more
core +5 more sources
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms [PDF]
, 2008 Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of ...
Filippone, M., Maurizio Filippone
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Unsupervised EA-Based Fuzzy Clustering for Image Segmentation
IEEE Access, 2020 This paper presents an unsupervised fuzzy clustering based on evolutionary algorithm for image segmentation. It needs no prior information about exact numbers of segments.
Mengxuan Zhang +4 more
doaj +1 more source
Statistical and fuzzy clustering methods and their application to clustering provinces of Iraq based on agricultural products [PDF]
AUT Journal of Mathematics and Computing, 2020 The important approaches to statistical and fuzzy clustering are reviewed and compared, and their applications to an agricultural problem based on a real-world data are investigated.
Israa Atiyah, Seyed Mahmoud Taheri
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RESAMPLING FOR FUZZY CLUSTERING [PDF]
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2007 Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster ...
openaire +3 more sources
Frontiers in Psychology, 2014 Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous.
Jocelyn H Bolin +3 more
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Random Fuzzy Clustering Granular Hyperplane Classifier
IEEE Access, 2020 Granular computing is a method of studying human intelligent information processing, which has advantage of knowledge discovery. In this paper, we convert a classification problem of sample space into a classification problem of fuzzy clustering granular
Wei Li +5 more
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Fuzzy Equivalence Relation Clustering Method Based on Constraint Conditions [PDF]
Jisuanji gongcheng, 2017 The traditional fuzzy equivalence relation clustering method cannot clusteraccording tospecific constraints,so that the clustering results have low accuracy,anddonot meet the requirement.In order to solve this problem,based on traditional fuzzy ...
LIANG Yuan,CHE Ming
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A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2020 Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data.
J. Arora, M. Tushir
doaj +1 more source