Efficient Mining of Correlation Patterns in Spatial Point Data

Abstract

We address the problem of analyzing spatial correlation between event types in large point data sets. Collocation rules are unsatisfactory, when confidence is not a sufficiently accurate interestingness measure, and Monte Carlo testing is infeasible, when the number of event types is large. We introduce an algorithm for mining correlation patterns, based on a non-parametric bootstrap test that, however, avoids the actual resampling by scanning each point and its distances to the events in the neighbourhood. As a real data set we analyze a large place name data set, the set of event types consisting of different linguistic features that appear in the place names. Experimental results show that the algorithm can be applied to large data sets with hundreds of event types.

Co-location Patterns, Interestingness Measures

“…Examples: digital road map (Shekhar and Ma), census data (Malerba et al 2001), place name data (Leino et al 2003;Salmenkivi 2006).…”

“…The correlation patterns introduced in Salmenkivi (2006) represent an intermediate approach between spatial point pattern analysis and co-location pattern mining. Correlation patterns are defined as interesting co-location patterns (in the event-centric model) of the form A !…”

Climate Extremes

“…Large spatial databases and spatial datasets. Examples: digital road map (Shekhar and Ma), census data (Malerba et al 2001), place name data (Leino et al 2003;Salmenkivi 2006).…”

A Delaunay diagram‐based Min–Max CP‐Tree algorithm for Spatial Data Analysis

“…Neighborhood‐based clustering discovered the clusters, based on the neighborhood characteristics of data. Prevalence measures were defined differently in different models of co‐locations mining . A user‐specified proximity neighborhood was used in place of transactions to specify groups of items.…”

“…Prevalence measures were defined differently in different models of co-locations mining. 18,19 A user-specified proximity neighborhood 20,21 was used in place of transactions to specify groups of items. A novel Joinless approach 22 for efficient co-location pattern mining used an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-location instances.…”