An effective clustering scheme for high-dimensional data

While the classical K -means algorithm has been widely used in many fields, it still has some defects. Therefore, this paper proposes a scheme to improve the clustering quality of K -means algorithm. The farthest initial center selection and the min–max rule are used to improve the random initializa...

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Veröffentlicht in:	Multimedia tools and applications Jg. 83; H. 15; S. 45001 - 45045
Hauptverfasser:	He, Xuansen, He, Fan, Fan, Yueping, Jiang, Lingmin, Liu, Runzong, Maalla, Allam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2024 Springer Nature B.V
Schlagworte:	Algorithms Cluster analysis Clustering Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Discriminant analysis Empirical analysis Multimedia Information Systems Normal distribution Special Purpose and Application-Based Systems Linear discriminant analysis Silhouette analysis Clustering validity function Initial center selection Empirical rule means algorithm
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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