K-means Clustering Algorithm and Its Improvement Research

Clustering is a typical unsupervised learning method, and it is also very important in natural language processing. K-means is one of the classical algorithms in clustering. In k-means algorithm, the processing mode of abnormal data and the similarity calculation method will affect the clustering di...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1873; H. 1; S. 12074
Hauptverfasser: Zhao, YanPing, Zhou, XiaoLai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.04.2021
Schlagworte:
Algorithms
Cluster analysis
Clustering
Euclidean geometry
Machine learning
Mathematical analysis
Natural language processing
Physics
Similarity
Vector quantization
ISSN:1742-6588, 1742-6596
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Zusammenfassung:Clustering is a typical unsupervised learning method, and it is also very important in natural language processing. K-means is one of the classical algorithms in clustering. In k-means algorithm, the processing mode of abnormal data and the similarity calculation method will affect the clustering division. Aiming at the defect of K-means, this paper proposes a new similarity calculation method, that is, a similarity calculation method based on weighted and Euclidean distance. Experiments show that the new algorithm is superior to k-means algorithm in efficiency, correctness and stability.
Bibliographie:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1873/1/012074