A Framework for Regional Association Rule Mining in Spatial Datasets

The immense explosion of geographically referenced data calls for efficient discovery of spatial knowledge. One critical requirement for spatial data mining is the capability to analyze datasets at different levels of granularity. One of the special challenges for spatial data mining is that information is usually not uniformly distributed in spatial datasets. Consequently, the discovery of regional knowledge is of fundamental importance for spatial data mining. Unfortunately, most of the current data mining techniques are ill-prepared for discovering regional knowledge. For example, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. This raises the questions on how to measure the interestingness of a set of regions and how to search effectively and efficiently for interesting regions. This paper centers on discovering regional association rules in spatial datasets. In particular,...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Wei Ding 0003, Christoph F. Eick, Jing Wang 0007, Xiaojing Yuan
• Keywords: Data Mining, ICDM 2006, Regional, Spatial Data Mining, Spatial Datasets