Discovering optimal clusters using firefly algorithm
Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge.A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of cl...
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| Format: | Article |
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Inderscience Enterprises Ltd.
2016
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| _version_ | 1803627940746887168 |
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| author | Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza |
| author_facet | Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza |
| author_sort | Mohammed, Athraa Jasim |
| collection | UUM |
| description | Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge.A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster.In this paper, we present the WFA_selection algorithm that originates from weight-based firefly algorithm.The newly proposed WFA_selection merges selected clusters in order to produce a better quality of clusters.Experiments utilising the WFA and WFA_selection algorithms were conducted on the 20Newsgroups and Reuters-21578 benchmark dataset and the output were compared against bisect K-means and general stochastic clustering method (GSCM).Results demonstrate that the WFA_selection generates a more robust and compact clusters as compared to the WFA, bisect K-means and GSCM. |
| first_indexed | 2024-07-04T06:14:03Z |
| format | Article |
| id | uum-20643 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:14:03Z |
| publishDate | 2016 |
| publisher | Inderscience Enterprises Ltd. |
| record_format | dspace |
| spelling | uum-206432017-01-18T03:35:03Z https://repo.uum.edu.my/id/eprint/20643/ Discovering optimal clusters using firefly algorithm Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza QA75 Electronic computers. Computer science Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge.A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster.In this paper, we present the WFA_selection algorithm that originates from weight-based firefly algorithm.The newly proposed WFA_selection merges selected clusters in order to produce a better quality of clusters.Experiments utilising the WFA and WFA_selection algorithms were conducted on the 20Newsgroups and Reuters-21578 benchmark dataset and the output were compared against bisect K-means and general stochastic clustering method (GSCM).Results demonstrate that the WFA_selection generates a more robust and compact clusters as compared to the WFA, bisect K-means and GSCM. Inderscience Enterprises Ltd. 2016 Article PeerReviewed Mohammed, Athraa Jasim and Yusof, Yuhanis and Husni, Husniza (2016) Discovering optimal clusters using firefly algorithm. International Journal of Data Mining, Modelling and Management, 8 (4). p. 330. ISSN 1759-1163 http://doi.org/10.1504/IJDMMM.2016.081239 doi:10.1504/IJDMMM.2016.081239 doi:10.1504/IJDMMM.2016.081239 |
| spellingShingle | QA75 Electronic computers. Computer science Mohammed, Athraa Jasim Yusof, Yuhanis Husni, Husniza Discovering optimal clusters using firefly algorithm |
| title | Discovering optimal clusters using firefly algorithm |
| title_full | Discovering optimal clusters using firefly algorithm |
| title_fullStr | Discovering optimal clusters using firefly algorithm |
| title_full_unstemmed | Discovering optimal clusters using firefly algorithm |
| title_short | Discovering optimal clusters using firefly algorithm |
| title_sort | discovering optimal clusters using firefly algorithm |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT mohammedathraajasim discoveringoptimalclustersusingfireflyalgorithm AT yusofyuhanis discoveringoptimalclustersusingfireflyalgorithm AT husnihusniza discoveringoptimalclustersusingfireflyalgorithm |