Genetic-Frog-Leaping Algorithm for Text Document Clustering  

作　　者：Lubna Alhenak Manar Hosny 

机构地区：[1]Computer Science Department,College of Computer and Information Sciences,King Saud University,Riyadh,Saudi Arabia

出　　处：《Computers, Materials & Continua》2019年第9期1045-1074,共30页计算机、材料和连续体（英文）

基　　金：This research was supported by a grant from the Research Center of the Center for Female Scientific and Medical Colleges Deanship of Scientific Research,King Saud University.

摘　　要：In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web.As a result,the use of techniques for extracting useful information from large collections of data,and particularly documents,has become more necessary and challenging.Text clustering is such a technique;it consists in dividing a set of text documents into clusters(groups),so that documents within the same cluster are closely related,whereas documents in different clusters are as different as possible.Clustering depends on measuring the content(i.e.,words)of a document in terms of relevance.Nevertheless,as documents usually contain a large number of words,some of them may be irrelevant to the topic under consideration or redundant.This can confuse and complicate the clustering process and make it less accurate.Accordingly,feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features.In this study,we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features.The proposed approach is based on two metaheuristic algorithms:a genetic algorithm(GA)and a shuffled frog-leaping algorithm(SFLA).The GA performs feature selection,and the SFLA performs clustering.To evaluate its effectiveness,the proposed approach was tested on a well-known text document dataset:the“20Newsgroup”dataset from the University of California Irvine Machine Learning Repository.Overall,after multiple experiments were compared and analyzed,it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering,compared with classical K-means clustering.Nevertheless,this improvement requires longer computational time.

关 键 词：Text documents clustering meta-heuristic algorithms shuffled frog-leaping algorithm genetic algorithm feature selection 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]