Open AccessThe Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering
Author(s) -
Xiaoxuan Wu,
Changjian Guo,
Tanglei Hu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1732/1/012074
Subject(s) - cluster analysis , cure data clustering algorithm , correlation clustering , canopy clustering algorithm , data stream clustering , computer science , single linkage clustering , fuzzy clustering , data mining , clustering high dimensional data , determining the number of clusters in a data set , artificial intelligence , pattern recognition (psychology) , algorithm
Selective clustering ensemble algorithm can eliminate the inferior quality clustering member’s influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms.