Determining number of clusters using firefly algorithm with cluster merging for text clustering
Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query.The content of online websites (text, blogs, chats, news,etc.) are dynamically updated, nevertheless relevant information on the changes made are not present. Such a scen...
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| Format: | Book Section |
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Springer International Publishing
2015
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| _version_ | 1825804015962685440 |
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| author | Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza |
| author2 | Zaman, Halimah Badioze |
| author_facet | Zaman, Halimah Badioze Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza |
| author_sort | Mohammed, Athraa Jasim |
| collection | UUM |
| description | Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query.The content of online websites (text, blogs, chats, news,etc.) are dynamically updated, nevertheless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a data collection.In this paper, a dynamic text clustering that utilizes Firefly algorithm is introduced.The proposed, aFAmerge, clustering algorithm automatically groups text documents into the appropriate number of clusters based on the behavior of firefly and cluster merging process. Experiments utilizing the proposed aFAmerge were conducted on two datasets; 20Newsgroups and Reuter’s news collection.Results indicate that the aFAmerge generates a more robust and compact clusters than the ones produced by Bisect K-means and practical General Stochastic Clustering Method (pGSCM). |
| first_indexed | 2024-07-04T06:07:30Z |
| format | Book Section |
| id | uum-18307 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:07:30Z |
| publishDate | 2015 |
| publisher | Springer International Publishing |
| record_format | eprints |
| spelling | uum-183072016-06-28T03:39:44Z https://repo.uum.edu.my/id/eprint/18307/ Determining number of clusters using firefly algorithm with cluster merging for text clustering Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza QA75 Electronic computers. Computer science Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query.The content of online websites (text, blogs, chats, news,etc.) are dynamically updated, nevertheless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a data collection.In this paper, a dynamic text clustering that utilizes Firefly algorithm is introduced.The proposed, aFAmerge, clustering algorithm automatically groups text documents into the appropriate number of clusters based on the behavior of firefly and cluster merging process. Experiments utilizing the proposed aFAmerge were conducted on two datasets; 20Newsgroups and Reuter’s news collection.Results indicate that the aFAmerge generates a more robust and compact clusters than the ones produced by Bisect K-means and practical General Stochastic Clustering Method (pGSCM). Springer International Publishing Zaman, Halimah Badioze Robinson, Peter Smeaton, Alan F. K. Shih, Timothy Velastin, Sergio Ahmad, Azizah Mohamad Ali, Nazlena 2015 Book Section PeerReviewed Mohammed, Athraa Jasim and Yusof, Yuhanis and Husni, Husniza (2015) Determining number of clusters using firefly algorithm with cluster merging for text clustering. In: Advances in Visual Informatics. Springer International Publishing, pp. 14-24. ISBN 978-3-319-25938-3 http://doi.org/10.1007/978-3-319-25939-0_2 doi:10.1007/978-3-319-25939-0_2 doi:10.1007/978-3-319-25939-0_2 |
| spellingShingle | QA75 Electronic computers. Computer science Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza Determining number of clusters using firefly algorithm with cluster merging for text clustering |
| title | Determining number of clusters using firefly algorithm with cluster merging for text clustering |
| title_full | Determining number of clusters using firefly algorithm with cluster merging for text clustering |
| title_fullStr | Determining number of clusters using firefly algorithm with cluster merging for text clustering |
| title_full_unstemmed | Determining number of clusters using firefly algorithm with cluster merging for text clustering |
| title_short | Determining number of clusters using firefly algorithm with cluster merging for text clustering |
| title_sort | determining number of clusters using firefly algorithm with cluster merging for text clustering |
| topic | QA75 Electronic computers. Computer science |
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