一种结合贪心选择和特征加权的高维数据聚类算法

doi:10.16180/j.cnki.issn1007-7820.2019.11.014

Abstract

Abstract:

In order to solve the problem that traditional clustering algorithms can not cluster high-dimensional data, a high-dimensional data clustering algorithm combining greedy selection and feature weighting was proposed. By clustering one-dimensional feature data, the clustering results of one-dimensional data were obtained first, and then all dimension data were clustered by adding dimension clustering weights to achieve clustering of high-dimensional data. The results showed that the algorithm can accurately cluster sparse high-dimensional data samples and meet the needs of high-dimensional data clustering processing, and had good practical application value.

Key words: greedy strategy, feature weighting, clustering, high-dimensional data, Mean shift

CLC Number:

TP392

XIANG Zhihua,SHAO Yali. A High Dimensional Data Clustering Algorithm Combining Greedy Selection and Feature Weighting[J].Electronic Science and Technology, 2019, 32(11): 70-73.

Figures/Tables 4

References 13

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半径	数据集1准确率/%	数据集2准确率/%
0.5	70.6	74.5
0.6	90.6	99.3
0.7	94.5	90.5
0.8	94.1	98.8
0.9	92.5	100.0
1.0	94.6	100.0

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A High Dimensional Data Clustering Algorithm Combining Greedy Selection and Feature Weighting

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