mining frequent neighboring class sets in spatial databases
We consider the problem of finding neighboring class sets. Objects of each instance of a neighboring class set are grouped using their Euclidean distances from each other. Recently, location-based services are growing along with mobile computing infrastructure such as cellular phones and PDAs. Therefore, we expect to see the development of spatial databases that contains very large number of access records including location information. The most typical type would be a database of point objects. Records of the objects may consist of "requested service name," "number of packet transmitted" in addition to x and y coordinate values indicating where the request came from. The algorithm presented here efficiently finds sets of "service names" that were frequently close to each other in the spatial database. For example, it may find a frequent neighboring class set, where "ticket" and "timetable" are frequently requested close to each other. By recognizing this, location-based service providers can promote a "ticket" service for customers who access the "timetable."
mining class spatial database database object distance locatability location-based service mobile computing infrastructure developer access records informal point coordination values algorithm examples custom