TY - GEN
T1 - Bayesian classification for spatial data using P-tree
AU - Hossain, Mohammad Kabir
AU - Alam, Rajibul
AU - Reaz, Abu Ahmed Sayeem
AU - Perrizo, William
PY - 2004/1/1
Y1 - 2004/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84935062833&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84935062833&partnerID=8YFLogxK
U2 - 10.1109/INMIC.2004.1492897
DO - 10.1109/INMIC.2004.1492897
M3 - Conference contribution
AN - SCOPUS:84935062833
T3 - Proceedings of INMIC 2004 - 8th International Multitopic Conference
SP - 321
EP - 327
BT - Proceedings of INMIC 2004 - 8th International Multitopic Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Multitopic Conference, INMIC 2004
Y2 - 24 December 2004 through 26 December 2004
ER -