Multidirectional knowledge extraction process for creating behavioral personas
Proceedings of the 10th Brazilian Symposium on on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction
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Text clustering is one of the difficult and hot research fields in the internet search engine research. Combination the advantages of K-means clustering and Self-Organizing Model (SOM) techniques, a new text clustering algorithm is presented. Firstly, texts are preprocessed to satisfy succeed process. Then, the paper analyzes common K-means clustering algorithm and SOM algorithm and combines them to overcome efficiency of low stability of K-means algorithm which is very sensitive to the initial cluster center and the isolated point text. The experimental results indicate that the improved algorithm has a higher accuracy and has a better stability, compared with the original algorithm.