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High Performance Data Mining Using the Nearest Neighbor Join

High Performance Data Mining Using the Nearest Neighbor Join,10.1109/ICDM.2002.1183884,Christian Böhm,Florian Krebs

High Performance Data Mining Using the Nearest Neighbor Join   (Citations: 22)
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The similarity join has become an important database primitive to support similarity search and data mining. A similarity join combines two sets of complex objects such that the result con- tains all pairs of similar objects. Well-known are two types of the similarity join, the distance range join where the user defines a distance threshold for the join, and the closest point query or k-distance join which retrieves the k most similar pairs. In this paper, we investigate an important, third similarity join opera- tion called k-nearest neighbor join which combines each point of one point set with its k nearest neighbors in the other set. It has been shown that many standard algorithms of Knowledge Dis- covery in Databases (KDD) such as k-means and k-medoid clus- tering, nearest neighbor classification, data cleansing, postpro- cessing of sampling-based data mining etc. can be implemented on top of the k-nn join operation to achieve performance im- provements without affecting the quality of the result of these al- gorithms. We propose a new algorithm to compute the k-nearest neighbor join using the multipage index (MuX), a specialized in- dex structure for the similarity join. To reduce both CPU and I/O cost, we develop optimal loading and processing strategies.
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