Adaptive firefly algorithm for hierarchical text clustering

Adaptive firefly algorithm for hierarchical text clustering

Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowle...

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Main Author: Mohammed, Athraa Jasim
Format: Thesis
Language:eng
eng
Published: 2016
Subjects:
QA75 Electronic computers
Computer science
Online Access:https://etd.uum.edu.my/5801/1/s94734_02.pdf
https://etd.uum.edu.my/5801/2/s94734_01.pdf
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institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Yusof, Yuhanis
Husni, Husniza
topic QA75 Electronic computers
Computer science
spellingShingle QA75 Electronic computers
Computer science
Mohammed, Athraa Jasim
Adaptive firefly algorithm for hierarchical text clustering
description Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowledge on the collection. Existing clustering methods have problems in determining optimal number of clusters and producing compact clusters. In this research, an adaptive hierarchical text clustering algorithm is proposed based on Firefly Algorithm. The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. Such an approach allows the relocation of a pre-assigned document into a newly created cluster. The third component, Weight-based Firefly Algorithm with Relocate and Merging (WFARM), aims to reduce the number of produced clusters by merging nonpure clusters into the pure ones. Experiments were conducted to compare the proposed algorithms against seven existing methods. The percentage of success in obtaining optimal number of clusters by AFA is 100% with purity and f-measure of 83% higher than the benchmarked methods. As for entropy measure, the AFA produced the lowest value (0.78) when compared to existing methods. The result indicates that Adaptive Firefly Algorithm can produce compact clusters. This research contributes to the text mining domain as hierarchical text clustering facilitates the indexing of documents and information retrieval processes.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Mohammed, Athraa Jasim
author_facet Mohammed, Athraa Jasim
author_sort Mohammed, Athraa Jasim
title Adaptive firefly algorithm for hierarchical text clustering
title_short Adaptive firefly algorithm for hierarchical text clustering
title_full Adaptive firefly algorithm for hierarchical text clustering
title_fullStr Adaptive firefly algorithm for hierarchical text clustering
title_full_unstemmed Adaptive firefly algorithm for hierarchical text clustering
title_sort adaptive firefly algorithm for hierarchical text clustering
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2016
url https://etd.uum.edu.my/5801/1/s94734_02.pdf
https://etd.uum.edu.my/5801/2/s94734_01.pdf
_version_ 1747827984964780032
spelling my-uum-etd.58012021-04-06T06:33:01Z Adaptive firefly algorithm for hierarchical text clustering 2016 Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA75 Electronic computers. Computer science Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowledge on the collection. Existing clustering methods have problems in determining optimal number of clusters and producing compact clusters. In this research, an adaptive hierarchical text clustering algorithm is proposed based on Firefly Algorithm. The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. Such an approach allows the relocation of a pre-assigned document into a newly created cluster. The third component, Weight-based Firefly Algorithm with Relocate and Merging (WFARM), aims to reduce the number of produced clusters by merging nonpure clusters into the pure ones. Experiments were conducted to compare the proposed algorithms against seven existing methods. The percentage of success in obtaining optimal number of clusters by AFA is 100% with purity and f-measure of 83% higher than the benchmarked methods. As for entropy measure, the AFA produced the lowest value (0.78) when compared to existing methods. The result indicates that Adaptive Firefly Algorithm can produce compact clusters. This research contributes to the text mining domain as hierarchical text clustering facilitates the indexing of documents and information retrieval processes. 2016 Thesis https://etd.uum.edu.my/5801/ https://etd.uum.edu.my/5801/1/s94734_02.pdf text eng public https://etd.uum.edu.my/5801/2/s94734_01.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia 20NewsgroupsDataSet. (2006). http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo- 4/text-learning/www/datasets.html. Abshouri, A. A., & Bakhtiary, A. (2012). A new clustering method based on Firefly and KHM. Journal of Communication and Computer, 9, 387–391. Retrieved from retrieved from: http://www.davidpublishing.com/davidpublishing/ Upfile/6/4/2012/2012060483417489.pdf Adaniya, M. H. A. C., Abr˜ao, T., & Proenc¸a Jr., M. L. (2013). Anomaly Detection Using Metaheuristic Firefly Harmonic Clustering. Journal of Networks, 8(1), 82–91. 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