Text Document Clustering Using Memetic Feature Selection
Ibraheem Al-Jadir, Kok Wai Wong, Chun Che Fung, Hong Ling Xie · 2017
With the wide increase of the volume of electronic documents, it becomes inevitable the need to invent more sophisticated machine learning methods to manage the issue. In this paper, a Memetic feature selection technique is proposed to improve the k-means and the spherical k-means clustering algorithms. The proposed Memetic feature selection technique combines the wrapper inductive method with the filter ranking method. The internal and external clustering evaluation measures are used to assess the resulted clusters. The test results showed that after using the proposed hybrid method, the resulted clusters were more accurate and more compacted in comparison to the clusters resulted from using the GA-selected feature or using the entire feature space.