Title :
Efficient disk-based K-means clustering for relational databases
Author :
Ordonez, Carlos ; Omiecinski, Edward
Author_Institution :
NCR Corp., Rancho Bernardo, CA, USA
Abstract :
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.
Keywords :
computational complexity; disc storage; pattern clustering; relational databases; storage management; very large databases; K-means algorithm; data sets; disk I/O; disk-based K-means clustering; memory requirements; relational databases; Algorithm design and analysis; Clustering algorithms; Data mining; Machine learning; Machine learning algorithms; Partitioning algorithms; Proposals; Relational databases; Sampling methods; Statistics; 65; Clustering; K-means; disk.; relational databases;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2004.25
