TY - GEN
T1 - Discovering spatial co-location patterns
T2 - 7th International Symposium on Spatial and Temporal Databases, SSTD 2001
AU - Shekhar, Shashi
AU - Huang, Yan
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2001
Y1 - 2001
N2 - Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type feature and a drought feature. The spatial co-location rule problem is different from the association rule problem. Even though boolean spatial feature types (also called spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. This creates difficulty in using traditional measures (e.g. support, confidence) and applying association rule mining algorithms which use support based pruning. We propose a notion of user-specified neighborhoods in place of transactions to specify groups of items. New interest measures for spatial co-location patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future.
AB - Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type feature and a drought feature. The spatial co-location rule problem is different from the association rule problem. Even though boolean spatial feature types (also called spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. This creates difficulty in using traditional measures (e.g. support, confidence) and applying association rule mining algorithms which use support based pruning. We propose a notion of user-specified neighborhoods in place of transactions to specify groups of items. New interest measures for spatial co-location patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future.
UR - http://www.scopus.com/inward/record.url?scp=84944053697&partnerID=8YFLogxK
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U2 - 10.1007/3-540-47724-1_13
DO - 10.1007/3-540-47724-1_13
M3 - Conference contribution
AN - SCOPUS:84944053697
SN - 9783540423010
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 236
EP - 256
BT - Advances in Spatial and Temporal Databases - 7th International Symposium, SSTD 2001, Proceedings
A2 - Jensen, Christian S.
A2 - Schneider, Markus
A2 - Seeger, Bernhard
A2 - Tsotras, Vassilis J.
PB - Springer Verlag
Y2 - 12 July 2001 through 15 July 2001
ER -