The P-tree algebra

Authors:
Qin Ding;Maleq Khan;Amalendu Roy;William Perrizo
Affiliations:
North Dakota State University, Fargo, ND;North Dakota State University, Fargo, ND;North Dakota State University, Fargo, ND;North Dakota State University, Fargo, ND
Venue:
Proceedings of the 2002 ACM symposium on Applied computing
Year:
2002

Citing 4
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Abstract

The Peano Count Tree (P-tree) is a quadrant-based lossless tree representation of the original spatial data. The idea of P-tree is to recursively divide the entire spatial data, such as Remotely Sensed Imagery data, into quadrants and record the count of 1-bits for each quadrant, thus forming a quadrant count tree. Using P-tree structure, all the count information can be calculated quickly. This facilitates efficient ways for data mining. In this paper, we will focus on the algebra and properties of P-tree structure and its variations. We have implemented fast algorithms for P-tree generation and P-tree operations. Our performance analysis shows P-tree has small space and time costs compared to the original data. We have also implemented some data mining algorithms using P-trees, such as Association Rule Mining, Decision Tree Classification and K-Clustering.