Text clustering based on fusion of ant colony and genetic algorithms

doi:10.1007/s11460-009-0019-9

Front Elect Electr Eng Chin

2009, Vol. 4

Issue (1) : 15-19 https://doi.org/10.1007/s11460-009-0019-9

RESEARCH ARTICLE

Text clustering based on fusion of ant colony and genetic algorithms

Yun ZHANG(

), Boqin FENG, Shouqiang MA, Lianmeng LIU

School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

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Abstract

Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space, a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed. The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes, thereby the fitness function, selection, crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration, and then it is applied to text clustering. The simulation results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm, the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%, 48.60% and 69.60%, respectively, in 3 test data sets. Therefore, it is more suitable for processing a larger dataset.

Keywords ant colony clustering genetic algorithm fusion text clustering

Corresponding Author(s): ZHANG Yun,Email:xjtu.cloud@gmail.com

Issue Date: 05 March 2009

Cite this article:

Boqin FENG,Shouqiang MA,Lianmeng LIU, et al. Text clustering based on fusion of ant colony and genetic algorithms[J]. Front Elect Electr Eng Chin, 2009, 4(1): 15-19.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-009-0019-9
https://academic.hep.com.cn/fee/EN/Y2009/V4/I1/15

Tab.1 Description of text dataset

Tab.2 -Measure value of 3 clustering algorithms on 3 datasets

Tab.3 SSE value of 3 clustering algorithms on 3 datasets

Fig.1 -Measure of different clustering algorithms

Fig.2 SSE of different clustering algorithms

Tab.4 Optimal -measure values obtained by ACGA (Initc=1)

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