Improved K‐means algorithm for clustering non‐spherical data-Reference-Cited by-同舟云学术

Improved K‐means algorithm for clustering non‐spherical data

Published:2022-07-22 Issue:9 Volume:39 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

He Honglei¹^ORCID,He Yuxuan²^ORCID,Wang Fang³,Zhu Wenming⁴^ORCID

Affiliation:

1. School of Information Engineering Lianyungang Technical College Lianyungang China

2. School of Mathematics and Statistics Xuzhou University of Technology Xuzhou China

3. Lianyungang TCM Branch of Jiangsu Union Technical Institute Lianyungang China

4. Department of Public Courses Shenzhen Institute of Information Technology Shenzhen China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13062

Reference38 articles.

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3. Chen H.(2013).Density‐accumulated arbitrary shaped clustering for large data sets.Proceedings of the 2nd international symposium on instrumentation and measurement sensor network and automation (IMSNA).IEEE pp.1088–1092.https://doi.org/10.1109/IMSNA.2013.6743470.

4. Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads

5. Esteves R. M. Hacker T. &Rong C. (2013).Competitive K‐means a new accurate and distributed K‐means algorithm for large datasets.Proceedings of the IEEE 5th international conference on cloud computing technology and science.IEEE pp.17‐24.https://doi.org/10.1109/CloudCom.2013.89.

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