The Document Clustering using Multi-Objective Genetic Algorithms
다목적 유전자 알고리즘을 이용한문서 클러스터링

Jung-Song Lee, Soon-Cheol Park
2012 Journal of the Korea Industrial Information Systems Research  
In this paper, the multi-objective genetic algorithm is proposed for the document clustering which is important in the text mining field. The most important function in the document clustering algorithm is to group the similar documents in a corpus. So far, the k-means clustering and genetic algorithms are much in progress in this field. However, the k-means clustering depends too much on the initial centroid, the genetic algorithm has the disadvantage of coming off in the local optimal value
more » ... cal optimal value easily according to the fitness function. In this paper, the multi-objective genetic algorithm is applied to the document clustering in order to complement these disadvantages while its accuracy is analyzed and compared to the existing algorithms. In our experimental results, the multi-objective genetic algorithm introduced in this paper shows the accuracy improvement which is superior to the k-means clustering(about 20 %) and the general genetic algorithm (about 17 %) for the document clustering.
doi:10.9723/jksiis.2012.17.2.057 fatcat:ojwqyx2dffbkjhnatafrhnquca