TITLE: Predicting Location Using Map Similarity: A Case Study in Mining
Spatial Data
PRESENTER:
Shashi Shekhar
AFFILIATION:
Computer Sc. Dept., CTS and AHPCRC, Univ. of Minnesota.
TECHNICAL ABSTRACT:
Spatial data mining is a process to discover interesting,
potentially useful, and high utility patterns embedded in spatial
databases.
Efficient tools for extracting information from spatial data sets can be
of
importance to organizations which own, generate, and manage
large spatial data sets. The current approach
towards solving spatial data mining problems is to use classical data
mining tools after ``materializing'' spatial relationships. However,
the key property of spatial data is that of spatial autocorrelation.
Like temporal data, spatial data values are influenced by
values in their immediate vicinity. Ignoring spatial autocorrelation
in the modeling process leads to results which are a poor-fit
and unreliable. In this paper we propose PLUMS (Predicting Locations
Using Map Similarity), a new approach for supervised spatial data mining
problems. PLUMS searches the space of solutions using a map-similarity
measure which is more appropriate in the context of spatial data.
We show that compared to state-of-the-art spatial statistics
approaches, PLUMS achives comparable accuracy but at a
fraction of the computational cost. Furthermore, PLUMS provides a general
framework for specializing other data mining techniques for mining
spatial
data.
KEYWORDS:
Data mining, Spatial Data Mining, Classification, habitats.
NOTE:
Some of the results discussed in this talk appeared in
papers in
Sanjay Chawla, Shashi Shekhar, Weili Wu and Uygar Ozesmi,
Modeling Spatial Dependencies for Mining Geospatial Data
Proc. Intl. Conference on Data Mining, SIAM, 2001.