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The fast clustering algorithm for the big data based on K-means.
- Source :
-
International Journal of Wavelets, Multiresolution & Information Processing . Nov2020, Vol. 18 Issue 6, pN.PAG-N.PAG. 15p. - Publication Year :
- 2020
-
Abstract
- As a powerful unsupervised learning technique, clustering is the fundamental task of big data analysis. However, many traditional clustering algorithms for big data that is a collection of high dimension, sparse and noise data do not perform well both in terms of computational efficiency and clustering accuracy. To alleviate these problems, this paper presents Feature K-means clustering model on the feature space of big data and introduces its fast algorithm based on Alternating Direction Multiplier Method (ADMM). We show the equivalence of the Feature K-means model in the original space and the feature space and prove the convergence of its iterative algorithm. Computationally, we compare the Feature K-means with Spherical K-means and Kernel K-means on several benchmark data sets, including artificial data and four face databases. Experiments show that the proposed approach is comparable to the state-of-the-art algorithm in big data clustering. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ALGORITHMS
*DATABASES
*K-means clustering
*SINGULAR value decomposition
Subjects
Details
- Language :
- English
- ISSN :
- 02196913
- Volume :
- 18
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- International Journal of Wavelets, Multiresolution & Information Processing
- Publication Type :
- Academic Journal
- Accession number :
- 147476960
- Full Text :
- https://doi.org/10.1142/S0219691320500538