Results 21 to 30 of about 2,884,172 (338)
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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Multimodal decision-level fusion for person authentication [PDF]
, 1999 In this paper, the use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM), fuzzy vector quantization ...
Bors, A.G., Chatzis, V., Pitas, I.
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IEEE Access, 2020 To prolong the function of wireless sensor networks (WSNs), the lifetime of the system has to be increased. WSNs lifetime can be calculated by using a few generic parameters, such as the time until the death of the first node and other parameters ...
Sonam Lata +3 more
semanticscholar +1 more source
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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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
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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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, 2015 Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative ...
Mackey, Lester +3 more
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AN APPROACH TO REMOVE THE EFFECT OF RANDOM INITIALIZATION FROM FUZZY C-MEANS CLUSTERING TECHNIQUE [PDF]
Journal of Process Management and New Technologies, 2014 Out of the different available fuzzy clustering techniques Bezdek’s Fuzzy C-Means clustering technique is among the most popular ones. Due to the random initialization of the membership values the performance of Fuzzy C-Means clustering technique ...
Samarjit Das, Hemanta K. Baruah
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A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data [PDF]
Journal of Artificial Intelligence and Data Mining, 2020 The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data.
J. Tayyebi, E. Hosseinzadeh
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Combination Evaluation Method of Fuzzy C-Mean Clustering Validity Based on Hybrid Weighted Strategy
IEEE Access, 2021 Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function.
H. Y. Wang, J. S. Wang, G. Wang
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