Parameter-free spatial data mining using MDL

Spiros Papadimitriou, Risto A. Väisänen, Aristides Gionis, Heikki Mannila, Panayiotis Tsaparas, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Pages346-353
Number of pages8
DOIs
StatePublished - 2005
Externally publishedYes
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: Nov 27 2005Nov 30 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other5th IEEE International Conference on Data Mining, ICDM 2005
Country/TerritoryUnited States
CityHouston, TX
Period11/27/0511/30/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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