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A Clustering Algorithm in Stream Data Using Strong Coreset.
- Source :
-
Journal of Interconnection Networks . 2022Supplement, Vol. 22, p1-21. 21p. - Publication Year :
- 2022
-
Abstract
- There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]
- Subjects :
- *K-means clustering
*DATA compression
*ALGORITHMS
*DATA mining
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 02192659
- Volume :
- 22
- Database :
- Academic Search Index
- Journal :
- Journal of Interconnection Networks
- Publication Type :
- Academic Journal
- Accession number :
- 159652123
- Full Text :
- https://doi.org/10.1142/S0219265921430118