An empirical analysis of techniques for constructing and searching k-dimensional trees

Affordable, fast computers with large memories have lessened the demand for program efficiency, but applications such as browsing and searching very large databases often have ratelimiting constraints and therefore benefit greatly from improvements in efficiency. This paper empirically evaluates several variants of a common k-dimensional tree technique to demonstrate how different algorithm options influence search cost for nearest neighbors. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval-- search process General Terms K-dimensional trees, nearest-neighbor search techniques.

• Type: Conference
• Year: 2000
• Where: KDD
• Authors: Douglas A. Talbert, Douglas H. Fisher
• Keywords: Data Mining, Influence Search Cost, K-dimensional Trees, KDD 2000, Nearest-neighbor Search Techniques