Results 11 to 20 of about 23,619,686 (300)
Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function. [PDF]
PLoS ONE, 2021 Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process.
Yaling Zhang, Jin Han
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Bilateral Weighted Fuzzy C-Means Clustering
Iranian Journal of Electrical and Electronic Engineering, 2012 Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise.
A. H. Hadjahmadi +2 more
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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.
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Fuzzy c-Means Clustering for Persistence Diagrams
CoRR, 2020 Version ...
Thomas O. M. Davies +3 more
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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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Performance comparison of fuzzy and non-fuzzy classification methods
Egyptian Informatics Journal, 2016 In data clustering, partition based clustering algorithms are widely used clustering algorithms. Among various partition algorithms, fuzzy algorithms, Fuzzy c-Means (FCM), Gustafson–Kessel (GK) and non-fuzzy algorithm, k-means (KM) are most popular ...
B. Simhachalam, G. Ganesan
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Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]
, 2009 Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Braak, C.J.F., ter +3 more
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Fuzzy clustering of time series gene expression data with cubic-spline [PDF]
, 2013 Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success.
Ali, Akhtar, Wang, Yu, Angelova, Maia
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Stock Data Clustering of Food and Beverage Company
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2007 Cluster analysis can be defined as identifying groups of similar objects to discover distribution of patterns and interesting correlations in large data sets.
Shofwatul Uyun, Subanar Subanar
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IEEE Access, 2020 Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious.
Yuan Huang +4 more
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