k-nearest Neighbor Classification on Spatial Data Streams Using P-trees

Abstract

Classification of spatial data streams is crucial, since the training dataset changes often. Building a new classifier each time can be very costly with most techniques. In this situation, k-nearest neighbor (KNN) classification is a very good choice, since no residual classifier needs to be built ahead of time. KNN is extremely simple to implement and lends itself to a wide variety of variations. We propose a new method of KNN classification for spatial data using a new, rich, data-mining-ready structure, the Peano-count-tree (P-tree). We merely perform some AND/OR operations on P-trees to find the nearest neighbors of a new sample and assign the class label. We have fast and efficient algorithms for the AND/OR operations, which reduce the classification time significantly. Instead of taking exactly the k nearest neighbors we form a closed-KNN set. Our experimental results show closed-KNN yields higher classification accuracy as well as significantly higher speed.

Patents are pending on the bSQ and Ptree technology.

This work is partially supported by NSF Grant OSR-9553368, DARPA Grant DAAH04-96-1-0329 and GSA Grant ACT#: K96130308.