Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 16, Issue 8, Pages 909-921Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2004.25
Keywords
clustering; K-means; relational databases; disk
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K-means is one of the most popular clustering algorithms. This article introduces 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.
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