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Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing
Joint Authors
Li, Chunzhong
Zhang, Yunong
Chen, Xu
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
As one of the typical clustering algorithms, heuristic clustering is characterized by its flexibility in feature integration.
This paper proposes a type of heuristic algorithm based on cognitive feature integration.
The proposed algorithm employs nonparameter density estimation and maximum likelihood estimation to integrate whole and local cognitive features and finally outputs satisfying clustering results.
The new approach possesses great expansibility, which enables priors supplement and misclassification adjusting during clustering process.
The advantages of the new approach are as follows: (1) it is effective in recognizing stable clustering results without priors given in advance; (2) it can be applied in complex data sets and is not restricted by density and shape of the clusters; and (3) it is effective in noise and outlier recognition, which does not need elimination of noises and outliers in advance.
The experiments on synthetic and real data sets exhibit better performance of the new algorithm.
American Psychological Association (APA)
Li, Chunzhong& Zhang, Yunong& Chen, Xu. 2019. Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1194397
Modern Language Association (MLA)
Li, Chunzhong…[et al.]. Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing. Mathematical Problems in Engineering No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1194397
American Medical Association (AMA)
Li, Chunzhong& Zhang, Yunong& Chen, Xu. Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1194397
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194397