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Spatial Data Mining: Progress and Challenges (1996)
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| Venue: | SIGMOD WORKSHOP ON RESEARCH ISSUES ON DATA MINING AND KNOWLEDGE DISCOVERY (DMKD |
| Citations: | 67 - 0 self |
BibTeX
@INPROCEEDINGS{Koperski96spatialdata,
author = {Krzysztof Koperski and Junas Adhikary and Jiawei Han},
title = {Spatial Data Mining: Progress and Challenges},
booktitle = {SIGMOD WORKSHOP ON RESEARCH ISSUES ON DATA MINING AND KNOWLEDGE DISCOVERY (DMKD},
year = {1996},
pages = {1--10},
publisher = {}
}
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Abstract
Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a highly demanding field because huge amounts of spatial data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environ- mental assessment and planning, etc. The collected data far exceeded human's ability to analyze. Recent studies on data mining have extended the scope of data mining from relational and transactional databases to spatial databases. This paper summarizes recent works on spatial data mining, from spatial data generalization, to spatial data clustering, mining spatial association rules, etc. It shows that spatial data mining is a promising field, with fruitful research results and many challenging issues.
Keyphrases
spatial data mining spatial data demanding field various application spatial association rule recent work huge amount geographical information system mental assessment large amount promising field remote sensing computer cartography recent study spatial database fruitful research result spatial data generalization data mining transactional database
