An Improved K-Means Clustering Algorithm Based on Feature Weighting
Ren Jiang · 2006
Clustering analysis is one of the important problems in the data mining and machine learning areas. Recently,feature selection and feature weighting methods are introduced to clustering algorithms for improving the clustering quality [1～3]. Inspired by the research,an improved k-means clustering based on feature weighting is proposed,which proposes a density-based initial centers search algorithm. The experiments show that the proposed algorithm can result in high quality clustering steadily.