%A Zhan Chunxia,Wang Rongbo,Huang Xiaoxi,Chen Zhiqun %T Application of Text Clustering Method Based on Improved CFSFDP Algorithm %0 Journal Article %D 2017 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.04.11 %P 94-99 %V 1 %N 4 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4359.shtml} %8 2017-04-25 %X

[Objective] This paper aims to improve the un-satisfactory performance of CFSFDP (clustering by fast search and find of density peaks) algorithm with the help of based on particle swarm optimization. [Methods] First, we determined the cluster centers by searching optimal local density and distance thresholds to increase the accuracy of results. These clustering centers have relatively high local density and distance, which reduced the influence of discrete points. Then, we examined the proposed method on a randomly selected dataset from the question-answer database of a college entrance exam consulting platform. [Results] The modified CFSFDP algorithm had better performance than the original one. [Limitations] We did not include the semantic relations to process the texts. [Conclusions] The proposed algorithm could achieve good clustering results, and improve the efficiency of the consulting personnel .