Results 131 to 140 of about 867,994 (193)
Construction of the Public Management Performance Assessment Algorithm Using Fuzzy Clustering.
J Environ Public Health, 2022 Zhang R.
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Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. [PDF]
Front PsychiatryEllis CA, Miller RL, Calhoun VD.
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Estimation curve of multivariate adaptive biresponse fuzzy clustering means regression splines approach to stunting and wasting cases in Southeast Sulawesi. [PDF]
MethodsXMeilisa M, Otok BW, Purnomo JDT.
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Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features. [PDF]
Sensors (Basel)Chan KY, Yiu KFC, Kim D, Abu-Siada A.
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On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
, 2017 Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set. This hesitation may be arise in the selection process of a suitable clustering (classification) algorithm and ...
Laya Aliahmadipour +2 more
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Quality and Reliability Engineering International, 2010
AbstractGrouping unknown data into groups of similar data is a necessary first step for classification, indexing of databases, and prediction. Most of the current applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or online.
Petra Perner, Anja Attig
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AbstractGrouping unknown data into groups of similar data is a necessary first step for classification, indexing of databases, and prediction. Most of the current applications, such as news classification, blog indexing, image classification, and medical diagnosis, obtain their data in temporal sequence or online.
Petra Perner, Anja Attig
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Information Sciences, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Peng 0001 +4 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong Peng 0001 +4 more
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Fuzzy clustering: Determining the number of clusters
2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), 2012In this study we analyze behavior of two types of coefficients for determining the suitable number of clusters obtained when fuzzy cluster analysis is applied. First one is Dunn's coefficient which contains membership degrees in its computational formula; second one is the average silhouette width, used primarily for evaluating hard clustering.
Hana Rezanková, Dusan Húsek
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Fuzzy clustering with supervision
Pattern Recognition, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Witold Pedrycz, George Vukovich
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Gravitational Fuzzy Clustering
2008Data clustering is an important part of cluster analysis. Numerous clustering algorithms based on various theories have been developed, and new algorithms continue to appear in the literature. In this paper, supposing that each cluster center is a gravity center and each data point has a constant mass, Newton's law of gravity is transformed from m/d2to
Orhan U., Hekim M., Ibrikci T.
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