An Efficient Density-based Approach for Data Mining Tasks

Authors:
Carlotta Domeniconi;Dimitrios Gunopulos
Affiliations:
George Mason University, Information and Software Engineering Department, 22030, Fairfax, VA, USA;University of California, Computer Science Department, 22030, Riverside, CA, USA
Venue:
Knowledge and Information Systems
Year:
2004

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Abstract

We propose a locally adaptive technique to address the problem of setting the bandwidth parameters 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.