Genetic-Frog-Leaping Algorithm for Text Document Clustering

作     者：Lubna Alhenak Manar Hosny 

作者机构：Computer Science DepartmentCollege of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第61卷第9期

页      面：1045-1074页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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 

主　　题：Text documents clustering meta-heuristic algorithms shuffled frog-leaping algorithm genetic algorithm feature selection 

摘      要：In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide *** a result,the use of techniques for extracting useful information from large collections of data,and particularly documents,has become more necessary and *** 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 *** depends on measuring the content(i.e.,words)of a document in terms of ***,as documents usually contain a large number of words,some of them may be irrelevant to the topic under consideration or *** can confuse and complicate the clustering process and make it less ***,feature selection methods have been employed to reduce data dimensionality by selecting the most relevant *** 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 *** 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 *** evaluate its effectiveness,the proposed approach was tested on a well-known text document dataset:the“20Newsgroupdataset from the University of California Irvine Machine Learning ***,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 ***,this improvement requires longer computational time.