A new text clustering algorithm based on improved k_means

作者：Xinwu Li^*

来源：Journal of Software, 2012, 7(1): 95-101.

DOI：10.4304/jsw.7.1.95-101

• Cluster texts
• Improved algorithm
• Initial cluster centers
• Internet search engine
• K-means
• k-Means algorithm
• K-means clustering
• New model
• Original algorithms
• Process requirements
• Research fields
• Seed selection
• Self-organizing model
• Text Clustering
• Text-clustering algorithm

Text clustering is one of the difficult and hot research fields in the internet search engine research. A new text clustering algorithm is presented based on Kmeans and Self-Organizing Model (SOM). Firstly, texts are preprocessed to satisfy succeed process requirement. Secondly, the paper improves selection of initial cluster centers and cluster seed selection methods of K-means to improve the deficiency of K-means algorithm that the Kmeans algorithm is very sensitive to the initial cluster center and the isolated point text. Thirdly the advantages of kmeans and SOM are combined to a new model to cluster text in the paper. Finally the experimental results indicate that the improved algorithm has a higher accuracy compared with the original algorithm, and has a better stability.

• 出版日期2012