Deriving High Confidence Rules from Spatial Data Using Peano Count Trees

Abstract

The traditional task of association rule mining is to find all rules with high support and high confidence. In some applications, such as mining spatial datasets for natural resource location, the task is to find high confidence rules even though the support may be low. In still other applications, such as the identification of agricultural pest infestations, the task is to find high confidence rules preferably while the support is still very low. The basic Apriori algorithm cannot be used to solve these problems efficiently since it relies on first identifying all high support itemsets. In this paper, we propose a new model to derive high confidence rules for spatial data regardless of their support level. A new data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. P-trees represent spatial data bit-by-bit in a recursive quadrant-by-quadrant arrangement. Based on the P-tree, we build a special data cube, the Tuple Count Cube (T-cube), to derive high confidence rules. Our algorithm for deriving confident rules is fast and efficient. In addition, we discuss some strategies for avoiding over-fitting (removing redundant and misleading rules).

This work was partially supported by a U. S. - G. S. A. VAST grant. Patents are pending on the P-Tree Data Mining Technology.