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Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications
Joint Authors
Wu, Yuehua
Qian, Guoqi
Ferrari, Davide
Qiao, Puxue
Hollande, Frédéric
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-26
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience.
It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes.
The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters.
Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model.
In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods.
We also provide a model selection based technique to determine the number of regression clusters underlying the data.
We further develop a computing procedure for regression clustering estimation and selection.
Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.
American Psychological Association (APA)
Qian, Guoqi& Wu, Yuehua& Ferrari, Davide& Qiao, Puxue& Hollande, Frédéric. 2016. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099672
Modern Language Association (MLA)
Qian, Guoqi…[et al.]. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1099672
American Medical Association (AMA)
Qian, Guoqi& Wu, Yuehua& Ferrari, Davide& Qiao, Puxue& Hollande, Frédéric. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099672
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099672