Multidimensional Circlet Lattice based Approach to Mine Spatially Co-Located Patterns

Abstract

With immense extent of applications, one of the important tasks in data mining is the spatial pattern identification. Discovering spatial co-location patterns presents challenges as spatial objects are embedded in a continuous space. For co-location pattern mining, previous studies often emphasize on the association analysis of every spatial feature. As a result, interesting patterns involving events with different frequency cannot be captured. In this paper, we study the problem of efficiently mining co-location patterns with lattice structure called, Multi-dimensional Circlet Lattice-based Spatial Co-location Pattern Mining (MCL-SCPM), frequently located together in spatial proximity. MCL-SCPM initially identifies the candidate co-location using feature inclusive ratio which incorporates the spatial co-location patterns at minimum time interval with maximum coverage ratio. Next, MCL-SCPM discovers co-location patterns in a multi-dimensional spatial structure for different movements of an object by measuring the cohesion of a pattern. To extract maximal co-located patterns, MCL-SCPM method finally, uses Circlet Lattice-based structure to extract maximal co-located patterns. We conduct an extensive performance study to test and evaluate the effectiveness of MCL-SCPM method. Our experimental results show that MCL-SCPM method minimizes the execution time for mining co-location patterns without producing large computational overheads.