Bayesian classification for spatial data using P-tree

Mohammad Kabir Hossain, Rajibul Alam, Abu Ahmed Sayeem Reaz, William Perrizo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Classification of spatial data can be difficult with existing methods due to the large numbers and sizes of spatial data sets and a large volume of data requires a huge amount of memory and/or time. The task becomes even more difficult when we consider continuous spatial data streams. In this paper, we deal with this challenge using the Peano Count Tree (P-tree), which provides a lossless, compressed, and data-mining-ready representation (data structure) for spatial data. We demonstrate how P-trees can improve the classification of spatial data when using a Bayesian classifier. We also introduce the use of information gain calculations with Bayesian classification to improve its accuracy. The use of a P-tree based Bayesian classifier can make classification, not only more effective on spatial data, but also can reduce the build time of the classifier considerably. This improvement in build time makes it feasible for use with streaming data.

Original languageEnglish (US)
Title of host publicationProceedings of INMIC 2004 - 8th International Multitopic Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)0780386809, 9780780386808
StatePublished - 2004
Externally publishedYes
Event8th International Multitopic Conference, INMIC 2004 - Lahore, Pakistan
Duration: Dec 24 2004Dec 26 2004

Publication series

NameProceedings of INMIC 2004 - 8th International Multitopic Conference


Conference8th International Multitopic Conference, INMIC 2004

Bibliographical note

Publisher Copyright:
© 2004 IEEE.


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