Abstract—A novel approach is presented in the paper. The main defect of traditional methods of fuzzy clustering is to know the number of clustering in advance. The text eigenvector is acquired based on the vector space model (VSM) and TF.IDF method. This paper applies information entropy to FRC algorithm. Random forest fulfils reduce dimension and initial classification result, which is used as input of FCM algorithm. In the iterative process, the number of clustering is ascertained until inforamtion entropy is minimum value. Due to FRC algorithm don’t need reduce dimension, the present algorithm possess higher precision and efficiency. The fuzzy clustering algorithm is suitable for dealing with the semantic variety and complexity. The example demonstrates the effectiveness of the present algorithm.
Index Terms—Fuzzy clustering, random forest, ext mining.
Xinqing Geng is with the College of Mathematics and Information Science, Anshan Normal University, Anshan, China (e-mail: gengxinqing@163.com).
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Cite:Xinqing Geng, "FRC: A New Dynamic Fuzzy Clustering Alogorithm Based on Random Forest Algorithm," International Journal of Computer Theory and Engineering vol. 10, no. 6, pp. 190-193, 2018.
