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Identification of air pollution patterns using a modified fuzzy co-occurrence pattern mining method
Akbari, M. & Samadzadegan, F.
Abstract
Spatio-temporal co-occurrence patterns represent
subsets of object types which are located together in
both space and time. Discovering spatio-temporal co-occurrence
patterns is an important task having many application
domains. There are a number of developed methods
to mine co-occurrence patterns; however, using them needs
a unique parameter to define the neighborhood. Identification
of a unique optimum k-value or neighborhood radius
is a challenging issue in different application domains. The
developed method of this research defines a new fuzzy
neighborhood and new fuzzy metrics to be applicable for
real applications such as air pollution, especially when the
researchers have no comprehensive knowledge regarding
the application domain; in addition, it mines patterns based
on the fuzzy nature of environmental phenomena. The new
method mines patterns locally without localization step to
speed up the mining process and considers all feature types
(point, line and polygon) to handle all applications. Subsequently,
it is applied to a real data set of Tehran city for
air pollution to discover significant co-occurrence patterns
of air pollution and influencing environmental parameters
such as meteorological, topography and traffic. The case
study results showed seven meaningful patterns among air
pollution classes 2 and 3 and wind speed class 1, topography
class 1 and traffic classes 1 and 2. The evaluation
confirmed the accuracy and applicability of the new
developed method for air pollution case. Furthermore, two
performance tests for the method itself and a performance
test against a crisp method were done, where the results
exhibited an efficient computational performance.
Keywords
Air pollution; Data mining; Co-occurrence pattern mining; Fuzzy; Tehran
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