Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data

Clustering as a fundamental unsupervised learning is considered an important method of data analysis, and -means is demonstrably the most popular clustering algorithm. In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by -means. Diff...

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Bibliographic Details
Published inApplied mathematics and nonlinear sciences Vol. 5; no. 1; pp. 1 - 10
Main Authors Xie, Ting, Liu, Ruihua, Wei, Zhengyuan
Format Journal Article
LanguageEnglish
Published Sciendo 01.01.2020
Subjects
62K86
Big Data
Clustering
Feature space
k-means
means
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Summary:Clustering as a fundamental unsupervised learning is considered an important method of data analysis, and -means is demonstrably the most popular clustering algorithm. In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by -means. Different from the traditional methods, the algorithm guaranteed the consistency of the clustering accuracy before and after descending dimension, accelerated -means when the clustering centeres and distance functions satisfy certain conditions, completely matched in the preprocessing step and clustering step, and improved the efficiency and accuracy. Experimental results have demonstrated the effectiveness of the proposed algorithm.
ISSN:2444-8656
2444-8656
DOI:10.2478/amns.2020.1.00001