DataMIME

INTRODUCTION DataMIME^TM (Data: Mine It More Efficiently) is a `universal' data mining system developed by the DataSURG research group at North Dakota State University. The system exploits a novel technology, the Ptree technology, for compressed vertical data representation which facilitates fast and efficient data mining over large datasets. DataMIME^TM provides a suite of data mining applications over the Internet for the tasks of association rule mining, classification, and the like. The key for using the Ptree technology in DataMIME^TM lies in its vertical data representation (i.e. column-based in contrast to the conventional horizontal rowbased representation employed by the relational model) and processing of data which has proven the efficiency and scalability of the applications embedded in DataMIME^TM. 2. VERTICAL PARTITIONING USING THE PTREE TECHNOLOGY The concept of vertical partitioning has been studied within the context of both centralized and distributed database s

The P-tree Algebra

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. Keywords Compression, Quadrant, Peano Ordering, Spatial Data, Tree Structure 1