Translated Title
A Semi-Supervised Clustering Algorithm Based on Class Labels and Pairwise Constraints
Translated Abstract
Semi-supervised clustering uses the samples＇＇ supervised information to aid unsupervised learning.The samples＇＇ supervised information include class labels information and pairwise constraints information(must-link constraints and cannot-link constraints). This paper presents a semi-supervised clustering algorithm based on class labels and pairwise constraints (PLG-SSC).The algorithm contains the advantages of the genetic algorithm, and makes good use of the preceding two aspects of supervised information to help unsupervised clustering.The results of experiments on the uci data sets confirm that PLG-SSC algorithm can improve the accuracy of clustering effectively, and that it is a promising semi-supervised clustering algorithm.
Translated Keyword
Class Labels
Genetic Algorithm
Pairwise Constraints
Semi-Supervised Clustering
Access Number
WF:perioarticaltxsc201205023
