Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

Co-location patterns, which capture the phenomenon that objects with certain labels are often located in close geographic proximity, are defined based on a support measure which quantifies the prevalence of a pattern candidate in the form of a label set. Existing support measures share the idea of counting the number of instances of a given label set
$C$
as its support, where an instance of
$C$
is an object set whose objects collectively carry all labels in
$C$
and are located close to one another. However, they suffer from various weaknesses, e.g., fail to capture all possible instances, or overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score which counts instances
fractionally
if they overlap. Fraction-Score captures all possible instances, and handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and anti-monotonic). We develop efficient algorithms to solve the co-location pattern mining problem defined with Fraction-Score. Furthermore, to obtain representative patterns, we develop an efficient algorithm for mining the maximal co-location patterns, which are those patterns without proper superset patterns. We conduct extensive experiments using real and synthetic datasets, which verified the superiority of our proposals.