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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Clustering algorithm, Dataset filtering, Data mining, Ontology construction |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Dr. Rahmah Mokhtar |
| Date Deposited: | 11 Jan 2022 09:03 |
| Last Modified: | 11 Jan 2022 09:03 |
| URI: | http://umpir.ump.edu.my/id/eprint/32993 |
| Download Statistic: | View Download Statistics |
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