Efficient disk-based K-means clustering for relational databases
K-means is one of the most popular clustering algorithms. We introduce an efficient disk-based implementation of K-means. The proposed algorithm is designed to work inside a relational database management system. It can cluster large data sets having very high dimensionality. In general, it only req...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2004-08, Vol.16 (8), p.909-921 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | Ordonez, C. Omiecinski, E. |
| description | K-means is one of the most popular clustering algorithms. We introduce an efficient disk-based implementation of K-means. The proposed algorithm is designed to work inside a relational database management system. It can cluster large data sets having very high dimensionality. In general, it only requires three scans over the data set. It is optimized to perform heavy disk I/O and its memory requirements are low. Its parameters are easy to set. An extensive experimental section evaluates quality of results and performance. The proposed algorithm is compared against the Standard K-means algorithm as well as the Scalable K-means algorithm. |
| doi_str_mv | 10.1109/TKDE.2004.25 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Cluster analysis Clustering Clustering algorithms Data mining disk Disks K-means Machine learning Machine learning algorithms Management systems Partitioning algorithms Proposals Relational data bases Relational databases Sampling methods Statistics |
| title | Efficient disk-based K-means clustering for relational databases |
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