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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 ... (Update)
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BibTeX entry: (Update)
@misc{ chawla-predicting,
author = "Sanjay Chawla and Shashi Shekhar",
title = "Predicting Locations Using Map Similarity(PLUMS): A Framework for Spatial
Data Mining",
url = "citeseer.ist.psu.edu/561418.html" }
Citations (may not include all citations):56 Signal Detection Theory and ROC analysis (context) - Egan - 1975
53 Statistical analysis of spatial point patterns (context) - Diggle - 1993
33 Spatial Econometrics: methods and models (context) - Anselin - 1988
16 A generalized Estimating Equations Approach for Spattally Co.. (context) - Albert, McShane - 1995
15 Routledge and Kegan Paul (context) - Could, at - 1985
13 Tutorial on database mining (context) - Agrawal - 1994
9 Knowledge discovery in spatial databases - Ester, Kriegel et al.
1 Revised Edition (context) - Cressie, for - 1993
1 Svm Data Concepts (context) - Cherkassky, Muller - 1998
Documents on the same site (http://www.cs.umn.edu/research/shashi-group/paper_list.html): More
Equivalence Classes of Direction Objects and Applications - Shekhar, Liu, Chawla (1999) (Correct)
Spatial Databases: Accomplishments and Research Needs - Shekhar, Chawla, Ravada.. (1997) (Correct)
Materialization Trade-Offs in Hierarchical Shortest Path.. - Shekhar, Fetterer, Goyal (1997) (Correct)
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