Parameter-Free Spatial Data Mining Using MDL

Consider spatial data consisting of a set of binary features taking values over a collection of spatial extents (grid cells). We propose a method that simultaneously finds spatial correlation and feature co-occurrence patterns, without any parameters. In particular, we employ the Minimum Description Length (MDL) principle coupled with a natural way of compressing regions. This defines what “good” means: a feature co-occurrence pattern is good, if it helps us better compress the set of locations for these features. Conversely, a spatial correlation is good, if it helps us better compress the set of features in the corresponding region. Our approach is scalable for large datasets (both number of locations and of features). We evaluate our method on both real and synthetic datasets.

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Spiros Papadimitriou, Aristides Gionis, Panayiotis Tsaparas, Risto A. Väisänen, Heikki Mannila, Christos Faloutsos
• Keywords: Better Compress, Data Mining, Feature Co-occurrence Pattern, ICDM 2005, Spatial Correlation