Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

186 Pages·2019·3.986 MB·English

by  Laith Mohammad Qasim Abualigah

Studies 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
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Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
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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
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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