Name
Abstract | ||
|---|---|---|
We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause-effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1080/08839514.2011.570153 | Applied Artificial Intelligence |
Keywords | Field | DocType |
association-rule mining,massive databases,geographical layer,data mining technique,fruitful exploratory tool,autonomous pattern detector,data-rich environment,data sets,real crime data set,massive crime,data mining techniques,exploratory analysis,detail algorithm,association rule mining,data mining | Data science,Data mining,Concept mining,Data stream mining,Crime data,Web mining,Computer science,Artificial intelligence,Cluster analysis,K-optimal pattern discovery,Machine learning | Journal |
Volume | Issue | ISSN |
25 | 5 | 0883-9514 |
Citations | PageRank | References |
2 | 0.37 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ickjai Lee | 1 | 372 | 44.05 |
| vladimir estivillcastro | 2 | 903 | 107.50 |