Title:
Semi-supervised-learning
Download
Description: Righteousness of a Euclidean distance and supervision of a mixture of new nearest neighbor calculation functions, thus the K-means algorithm applied to the semi-supervised clustering problem. K-means algorithm the initial center of mass-sensitive defect clustering in the search space of the particle swarm algorithm simulation in Euclidean space, an iterative search to find the optimum cluster centroid, and strategies to improve particle swarm optimization to dynamic management of stocks search efficiency. Algorithm on multiple datasets in the UCI tests are a good clustering accuracy.
To Search:
File list (Check if you may need any files):
基于半监督学习的K-均值聚类算法研究.pdf
