DMCA
Spatial Interestingness Measures for Co-location Pattern Mining
| Citations: | 1 - 0 self |
BibTeX
@MISC{Sengstock_spatialinterestingness,
author = {Christian Sengstock and Michael Gertz and Tran Van Canh},
title = {Spatial Interestingness Measures for Co-location Pattern Mining},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract—Co-location pattern mining aims at finding sub-sets of spatial features frequently located together in spatial proximity. The underlying motivation is to model the spatial correlation structure between the features. This allows to discover interesting co-location rules (feature interactions) for spatial analysis and prediction tasks. As in association rule mining, a major problem is the huge amount of possible patterns and rules. Hence, measures are needed to identify interesting patterns and rules. Existing approaches so far focused on finding frequent patterns, patterns including rare features, and patterns occurring in small (local) regions. In this paper, we present a new general class of interest-ingness measures that are based on the spatial distribution of co-location patterns. These measures allow to judge the interestingness of a pattern based on properties of the un-derlying spatial feature distribution. The results are different from standard measures like participation index or confidence. To demonstrate the usefulness of these measures, we apply our approach to the discovery of rules on a subset of the OpenStreetMap point-of-interest data. Keywords-Co-location pattern mining, interestingness mea-sures, density estimation I.
Keyphrases
co-location pattern mining spatial interestingness measure keywords-co-location pattern mining interestingness mea-sures spatial correlation structure major problem density estimation rare feature possible pattern prediction task new general class underlying motivation participation index huge amount co-location pattern association rule mining interesting pattern spatial distribution spatial feature abstract co-location pattern mining aim frequent pattern feature interaction spatial proximity openstreetmap point-of-interest data spatial analysis interest-ingness measure un-derlying spatial feature distribution standard measure interesting co-location rule
