統合化地理情報システムからのデータマイニング : 近接クラス集合の発掘(<特集>データ・テキストマイニング)
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概要
- 論文の詳細を見る
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. For example, we have a database containing large number of access records of a location-based service. 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."
- 日本応用数理学会の論文
- 2002-12-25