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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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1873; no. 1; p. 12074
Main Authors Zhao, YanPing, Zhou, XiaoLai
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.04.2021
Subjects
Algorithms
Cluster analysis
Clustering
Euclidean geometry
Machine learning
Mathematical analysis
Natural language processing
Physics
Similarity
Vector quantization
Online AccessGet full text
ISSN1742-6588
1742-6596
1742-6596
DOI10.1088/1742-6596/1873/1/012074

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Summary: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.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
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ISSN:1742-6588
1742-6596
1742-6596
DOI:10.1088/1742-6596/1873/1/012074