K-Means: An Efficient Clustering Algorithm with Adaptive Decision Boundaries

Conventional k -means algorithms often face significant computational burdens and have a high dependence on the number of predefined clusters k . Therefore, this paper proposes the k ∗ -means algorithm, which incorporates the concept of the perceptron classification algorithm to transform the distan...

Full description

Bibliographic Details

Published in:	International journal of parallel programming Vol. 53; no. 1; p. 3
Main Authors:	Long, Jianwu, Liu, Luping
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2025 Springer Nature B.V
Subjects:	Adaptive algorithms Algorithms Classification Cluster analysis Clustering Computer Science Datasets Parallel processing Processor Architectures Software Engineering/Programming and Operating Systems Synthetic data Theory of Computation Hierarchical clustering Perceptron algorithm Means
ISSN:	0885-7458, 1573-7640
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!