Table Of ContentStudies in Computational Intelligence 816
Laith Mohammad Qasim Abualigah
Feature Selection
and Enhanced Krill
Herd Algorithm
for Text Document
Clustering
Studies in Computational Intelligence
Volume 816
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
The books of this series are submitted to indexing to Web of Science,
EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.
More information about this series at http://www.springer.com/series/7092
Laith Mohammad Qasim Abualigah
Feature Selection
and Enhanced Krill Herd
Algorithm for Text
Document Clustering
123
LaithMohammad Qasim Abualigah
Universiti Sains Malaysia
Penang,Malaysia
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-030-10673-7 ISBN978-3-030-10674-4 (eBook)
https://doi.org/10.1007/978-3-030-10674-4
LibraryofCongressControlNumber:2018965455
©SpringerNatureSwitzerlandAG2019
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar
methodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom
therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
The publisher, the authors, and the editorsare safeto assume that the adviceand informationin this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor
for any errors or omissions that may have been made. The publisher remains neutral with regard to
jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
List of Publications
Journal
1. Abualigah,L.M.,Khader,A.T.,Al-Betar,M.A.,Alomari,O.A.:Textfeature
selectionwitharobustweightschemeanddynamicdimensionreductiontotext
document clustering, (2017). Expert Systems with Applications. Elsevier.
(IF:3.928).
2. Abualigah, L. M., Khader, A. T., Hanandeh, E. S., Gandomi, A. H.: A novel
hybridization strategy for krill herd algorithm applied to clustering techniques,
(2017). Applied Soft Computing. Elsevier. (IF:3.541).
3. Abualigah, L. M., Khader, A. T., Hanandeh, E. S.: A new feature selection
method to improve the document clustering using particle swarm optimization
algorithm, (2017). Journal of Computational Science. Elsevier. (IF:1.748).
4. Abualigah,L.M.,Khader,A.T.:Unsupervisedtextfeatureselectiontechnique
based on hybrid particle swarm optimization algorithm with genetic operators
forthetextclustering,(2017).JournalofSupercomputing.Springer.(IF:1.326).
5. Bolaji,A.L.A.,Al-Betar,M.A.,Awadallah,M.A.,Khader,A.T.,Abualigah,
L.M.:Acomprehensivereview:KrillHerdalgorithm(KH)anditsapplications,
(2016). Applied Soft Computing. Elsevier. (IF:3.541).
6. Abualigah,L.M.,Khader,A.T.,Al-Betar,M.A.,Hanandeh,E.S.,Alyasseri,
Z.A.:Ahybridstrategyforkrillherdalgorithmwithharmonysearchalgorithm
to improve the data clustering, (2017). Intelligent Decision Technologies. IOS
Press. (Accepted).
7. Abualigah,L.M.,Khader,A.T.,Hanandeh,E.S.,Gandomi,A.H.:AHybrid
Krill Herd Algorithm and K-mean Algorithm for Text Document Clustering
analysis. Engineering Applications of Artificial Intelligence. Elsevier. (Under
3rd revision). (IF: 2.894).
8. Abualigah, L. M., Khader, A. T., Hanandeh, E. S.: Multi-objective modified
krill herd algorithm for intelligent text document clustering. Information
Systems and Applications. Springer. (Under review). (IF:1.530).
9. Abualigah, L. M., Khader, A. T., Hanandeh, E. S., Rehman, S. U., Shandilya,
S. K.: b-HILL CLIMBING TECHNIQUE FOR IMPROVING THE TEXT
DOCUMENT CLUSTERING PROBLEM. Current Medical Imaging Reviews.
(Under review). (IF:0.308).
v
vi ListofPublications
Chapter
1. Abualigah,L.M.,Khader,A.T.,Hanandeh,E.S.:Anovel weighting scheme
applied to improve the text document clustering techniques. Book Series:
Studies in Computational Intelligence published by Springer. Book Title:
Innovative Computing, Optimization and Its Applications. Springer.
2. Abualigah, L. M., Khader, A. T., Hanandeh, E. S.: Modified Krill Herd
AlgorithmforGlobalNumericalOptimizationProblems.BookTitle:Advances
in Nature-inspired Computing and Applications. Springer. (Accepted).
Conference
1. Abualigah, L. M., Khader, A. T., Al-Betar, M. A., Awadallah, M. A.: A krill
herdalgorithmforefficienttextdocumentsclustering.InComputerApplications
and Industrial Electronics (ISCAIE), (2016) IEEE Symposium on (pp. 67–72).
IEEE.
2. Abualigah, L. M., Khader, A. T., Al-Betar, M. A.: Unsupervised feature
selection technique based on genetic algorithm for improving the Text
Clustering. In Computer Science and Information Technology (CSIT), (2016)
7th International Conference on (pp. 1–6). IEEE.
3. Abualigah, L. M., Khader, A. T., Al-Betar, M. A.: Unsupervised feature
selection technique based on harmony search algorithm for improving the Text
Clustering. In Computer Science and Information Technology (CSIT), (2016)
7th International Conference on (pp. 1–6). IEEE.
4. Abualigah,L.M.,Khader,A.T.,Al-Betar,M.A.:Multi-objectives-based text
clustering technique using K-mean algorithm. In Computer Science and
Information Technology (CSIT), (2016) 7th International Conference on
(pp. 1–6). IEEE.
5. Abualigah, L. M., Khader, A. T., Al-Betar, M. A., Hanandeh, E. S.:
Unsupervised Text Feature Selection Technique Based on Particle Swarm
Optimization Algorithm for Improving the Text Clustering. First EAI
International Conference on Computer Science and Engineering (2017). EAI.
6. Abualigah, L. M., Khader, A. T., Al-Betar, M. A., Hanandeh, E. S.: A new
hybridization strategy for krill herd algorithm and harmony search algorithm
applied to improve the data clustering. First EAI International Conference on
Computer Science and Engineering, (2017). EAI.
ListofPublications vii
7. Abualigah, L. M., Khader, A. T., Al-Betar, M. A., Alyasseri Z. A., Alomari,
O.A.,Hanandeh,E.S.:FeatureSelectionwithb-Hillclimbing Search forText
Clustering Application. Second Palestinian International Conference on
Information and Communication Technology, (2017). IEEE.
8. Abualigah, L. M., Sawaiez, A. M., Khader, A. T., Rashaideh, H., Al-Betar,
M. A.: b-Hill Climbing Technique for the Text Document Clustering. New
Trends in Information Technology (NTIT), (2017). IEEE.
Acknowledgements
BeginningthisPh.D.thesishasbeenalife-changingexperienceformeandIwould
not have achieved it without the guidance and support of many people. I must
declare many external donations from individuals, who extended a helping hand
throughout this study.
IamthankfultoAllahSWTforgivingmestrengthtofinishthisstudy.Iamalso
grateful to my supervisor, Prof. Dr. Ahamad Tajudin Khader from the School of
Computer Sciences at Universiti Sains Malaysia, for his wise counsel, helpful
advice, connected support, and supervision throughout the duration of this study.
I am also thankful to my co-supervisor, Dr. Mohammed Azmi Al-Betar from the
Department of Information Technology, Al-Huson University College, Al-Balqa
Applied University, for his assistance. I am also thankful to Dr. Essam Said
Hanandeh from the Department of Computer Information System, Zarqa
University, for his assistance.
My family deserves special thanks. Words cannot express how grateful I am to
myfather,mother,andbrothersforallofthesacrificesthattheyhavedoneforme.
Finally, I thank all of my friends who encouraged me throughout the duration of
this study.
ix
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation and Problem Statement . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.5 Research Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.6 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.7 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Krill Herd Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Krill Herd Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Why the KHA has been Chosen for Solving the TDCP . . . . . . . 11
2.4 Krill Herd Algorithm: Procedures . . . . . . . . . . . . . . . . . . . . . . . 12
2.4.1 Mathematical Concept of Krill Herd Algorithm . . . . . . . 12
2.4.2 The Genetic Operators . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Literature Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Text Document Clustering Applications. . . . . . . . . . . . . . . . . . . 22
3.4 Variants of the Weighting Schemes . . . . . . . . . . . . . . . . . . . . . . 22
3.5 Similarity Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.5.1 Cosine Similarity Measure . . . . . . . . . . . . . . . . . . . . . . 26
3.5.2 Euclidean Distance Measure . . . . . . . . . . . . . . . . . . . . . 27
3.6 Text Feature Selection Method . . . . . . . . . . . . . . . . . . . . . . . . . 27
xi
