Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering
In the field of computer science, data mining facilitates the extraction of useful knowledge and patterns from a huge amount of data. Various techniques exist in the data mining domain to explore the links, associations, and patterns from data in data warehouses. Among these techniques, clustering...
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| Format: | Article |
| Language: | en |
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IJCSNS
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32993/1/analysis%20of%20kmean.pdf http://umpir.ump.edu.my/id/eprint/32993/ http://paper.ijcsns.org/07_book/202110/20211040.pdf |
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| author | Rahmah, Mokhtar Raza, Muhammad Ahsan Fauziah, Zainuddin Nor Azhar, Ahmad Raza, Muhammad Fahad Raza, Binish |
| author_facet | Rahmah, Mokhtar Raza, Muhammad Ahsan Fauziah, Zainuddin Nor Azhar, Ahmad Raza, Muhammad Fahad Raza, Binish |
| author_sort | Rahmah, Mokhtar |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In the field of computer science, data mining facilitates the
extraction of useful knowledge and patterns from a huge amount of data. Various techniques exist in the data mining domain to explore the links, associations, and patterns from data in data warehouses. Among these techniques, clustering is more prominent in analyzing raw and unlabeled data from a large volume of datasets. The clustering mechanism identifies similar features between data objects and arranges them into clusters. In this paper, we have compared the performance of K-Mean and XMean clustering algorithms using two datasets of student enrollment in higher education institutions. Our methodology incorporated ontology to filter the datasets and exploited Rapidminer environment to evaluate the performance of clustering algorithms. The results showed that X-Mean is more suitable for large datasets in terms of discovery and accuracy of clusters. |
| format | Article |
| id | my.ump.umpir.32993 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2021 |
| publisher | IJCSNS |
| record_format | eprints |
| spelling | my.ump.umpir.329932022-01-11T09:03:05Z http://umpir.ump.edu.my/id/eprint/32993/ Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering Rahmah, Mokhtar Raza, Muhammad Ahsan Fauziah, Zainuddin Nor Azhar, Ahmad Raza, Muhammad Fahad Raza, Binish QA75 Electronic computers. Computer science In the field of computer science, data mining facilitates the extraction of useful knowledge and patterns from a huge amount of data. Various techniques exist in the data mining domain to explore the links, associations, and patterns from data in data warehouses. Among these techniques, clustering is more prominent in analyzing raw and unlabeled data from a large volume of datasets. The clustering mechanism identifies similar features between data objects and arranges them into clusters. In this paper, we have compared the performance of K-Mean and XMean clustering algorithms using two datasets of student enrollment in higher education institutions. Our methodology incorporated ontology to filter the datasets and exploited Rapidminer environment to evaluate the performance of clustering algorithms. The results showed that X-Mean is more suitable for large datasets in terms of discovery and accuracy of clusters. IJCSNS 2021-10 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32993/1/analysis%20of%20kmean.pdf Rahmah, Mokhtar and Raza, Muhammad Ahsan and Fauziah, Zainuddin and Nor Azhar, Ahmad and Raza, Muhammad Fahad and Raza, Binish (2021) Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering. International Journal of Computer Science and Network Security, 21 (10). pp. 283-287. ISSN 1738-7906. (Published) http://paper.ijcsns.org/07_book/202110/20211040.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Rahmah, Mokhtar Raza, Muhammad Ahsan Fauziah, Zainuddin Nor Azhar, Ahmad Raza, Muhammad Fahad Raza, Binish Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering |
| title | Analysis of K-Mean and X-Mean Clustering Algorithms Using
Ontology-Based Dataset Filtering |
| title_full | Analysis of K-Mean and X-Mean Clustering Algorithms Using
Ontology-Based Dataset Filtering |
| title_fullStr | Analysis of K-Mean and X-Mean Clustering Algorithms Using
Ontology-Based Dataset Filtering |
| title_full_unstemmed | Analysis of K-Mean and X-Mean Clustering Algorithms Using
Ontology-Based Dataset Filtering |
| title_short | Analysis of K-Mean and X-Mean Clustering Algorithms Using
Ontology-Based Dataset Filtering |
| title_sort | analysis of k-mean and x-mean clustering algorithms using
ontology-based dataset filtering |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/32993/1/analysis%20of%20kmean.pdf http://umpir.ump.edu.my/id/eprint/32993/ http://paper.ijcsns.org/07_book/202110/20211040.pdf |
| url_provider | http://umpir.ump.edu.my/ |