A semi-supervised clustering algorithm based on rough reduction
Liandong Lin, Wei Qu, Xiang Yu · 2009
Clustering analysis is an important issue in data mining fields. Clustering in high dimensional space is especially difficult for a series of problems, such as the sparseness of spatial distribution of data, too much noise data points. Based on the analysis of current clustering algorithms can not get satisfying clustering results of high dimensional data. The theory of rough set and the idea of semi-supervised are introduced. And a semi-supervised grid clustering algorithm RSGrid based on the reduction of rough set theory is proposed. The theoretical analysis and experimental results indicate the algorithm can solve the problem of clustering in high dimensional space efficiently.