TY - GEN
T1 - Discovery of spatial association rules in geographic information databases
AU - Koperski, Krzysztof
AU - Han, Jiawei
PY - 1995
Y1 - 1995
N2 - Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data-and knowledge-bases. In this paper, an efficient method for mining strong spatial association rules in geographic information databases is proposed and studied. A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some nonspatial predicates. A strong rule indicates that the patterns in the rule have relatively frequent occurrences in the database and strong implication relationships. Severed optimization techniques are explored, including a two-step spatial computation technique (approximate computation on large sets, and refined computations on small promising patterns), shared processing in the derivation of large predicates at multiple concept levels, etc. Our analysis shows that interesting association rules can be discovered efficiently in large spatial databases.
AB - Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data-and knowledge-bases. In this paper, an efficient method for mining strong spatial association rules in geographic information databases is proposed and studied. A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some nonspatial predicates. A strong rule indicates that the patterns in the rule have relatively frequent occurrences in the database and strong implication relationships. Severed optimization techniques are explored, including a two-step spatial computation technique (approximate computation on large sets, and refined computations on small promising patterns), shared processing in the derivation of large predicates at multiple concept levels, etc. Our analysis shows that interesting association rules can be discovered efficiently in large spatial databases.
UR - http://www.scopus.com/inward/record.url?scp=84957645397&partnerID=8YFLogxK
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U2 - 10.1007/3-540-60159-7_4
DO - 10.1007/3-540-60159-7_4
M3 - Conference contribution
AN - SCOPUS:84957645397
SN - 3540601597
SN - 9783540601593
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 66
BT - Advances in Spatial Databases - 4th International Symposium, SSD 1995, Proceedings
A2 - Herring, John R.
A2 - Egenhofer, Max J.
PB - Springer
T2 - 4th International Symposium on Large Spatial Databases, SSD 1995
Y2 - 6 August 1995 through 9 August 1995
ER -