An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large Datasets
Carlotta Domeniconi, Dimitrios Gunopulos · 2001
We propose a locally adaptive technique to address the problem of setting the bandwidth parameters optimally for kernel density estimation. Our technique is efficient and can be performed in only two dataset passes. We also show how to apply our technique to efficiently solve range query approximation, classification and clustering problems for very large datasets. We validate the efficiency and accuracy of our technique by presenting experimental results on a variety of both synthetic and real datasets. 1.