Semi-supervised Clustering Ensemble with Pairwise Constraints Based on PCA Dimension Reduction

Abstract

Abstract: Aiming at the problem that the existing clustering integration algorithms are unsupervised and cannot deal with high-dimensional data well, this paper proposes a semi-supervised clustering ensemble with pairwise constraints based on PCA dimension reduction (SSCEDR), the SSCEDR method uses PCA principal component analysis to reduce the dimension of the original data. Combined with semi-supervised clustering integration technology, the prior knowledge such as pairwise constraints is substituted into the clustering integration process in the reduced dimension space. The effectiveness of the algorithm is verified by experiments on multiple data sets.

Key words: clustering ensemble, dimension reduction, pairwise constraints, semi-supervised, principal component analysis

HUANG Xin-chen, GAO Jun, HUANG Hao-jie. Semi-supervised Clustering Ensemble with Pairwise Constraints Based on PCA Dimension Reduction [J]. Computer and Modernization, 2021, 0(01): 94-99.

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Semi-supervised Clustering Ensemble with Pairwise Constraints Based on PCA Dimension Reduction

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