  In this paper, we introduce a novel graph-based method to classify the
regions with similar climate in a local area. We refer our proposed method as
Graph Partition Based Method (GPBM). Our proposed method attempts to overcome
the shortcomings of the current state-of-the-art methods in the literature. It
has no limit on the number of variables that can be used and also preserves the
nature of climate data. To illustrate the capability of our proposed algorithm,
we benchmark its performance with other state-of-the-art climate classification
techniques. The climate data is collected from 24 synoptic stations in Fars
province in southern Iran. The data includes seven climate variables stored as
time series from 1951 to 2017. Our results exhibit that our proposed method
performs a more realistic climate classification with less computational time.
It can save more information during the climate classification process and is
therefore efficient in further data analysis. Furthermore, using our method, we
can introduce seasonal graphs to better investigate seasonal climate changes.
To the best of our knowledge, our proposed method is the first graph-based
climate classification system.
