Decision tree classification of spatial data streams using Peano Count Trees

Many organizations have large quantities of spatial data collected in various application areas, including remote sensing, geographical information systems (GIS), astronomy, computer cartography, environmental assessment and planning, etc. These data collections are growing rapidly and can therefore be considered as spatial data streams. For data stream classification, time is a major issue. However, these spatial data sets are too large to be classified effectively in a reasonable amount of time using existing methods. In this paper, we developed a new method for decision tree classification on spatial data streams using a data structure called Peano Count Tree (P-tree). The Peano Count Tree is a spatial data organization that provides a lossless compressed representation of a spatial data set and facilitates efficient classification and other data mining techniques. Using P-tree structure, fast calculation of measurements, such as information gain, can be achieved. We compare P-tree...

• Type: Journal
• Year: 2002
• Where: SAC
• Authors: Qiang Ding, Qin Ding, William Perrizo
• Keywords: Applied Computing, Decision Tree Induction, SAC 2002, Spatial Data, Spatial Data Streams