Discovery of Areas with Locally Maximal Confidence from Location Data

Hiroya Inakoshi, Hiroaki Morikawa, Tatsuya Asai, Nobuhiro Yugami, Seishi Okamoto

Abstract: A novel algorithm is presented for discovering areas having locally maximized confidence of an association rule on a collection of location data. Although location data obtained from GPS-equipped devices have promising applications, those GPS points are usually not uniformly distributed in two-dimensional space. As a result, substantial insights might be missed by using data mining algorithms that discover admissible or rectangular areas under the assumption that the GPS data points are distributed uniformly. The proposed algorithm composes transitively connected groups of irregular meshes that have locally maximized confidence. There is thus no need to assume the uniformity, which enables the discovery of areas not limited to a certain class of shapes. Iterative removal of the meshes in accordance with the local maximum property enables the algorithm to perform 50 times faster than state-of-the-art ones.