SOM++: integration of self-organizing map and k-means++ algorithms
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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A two level clustering approach has been proposed in this paper in order to perform a classification analysis of meteorological data of Annaba region (North-East of Algeria) using data from 1995 to 1999. The Kohonen self-organizing map (SOM) has been used to group the data and produce the meteorological prototypes. The number of prototypes of SOM is large, to facilitate quantitative analysis of the map and the data similar units need to be grouped (clustered). As a second clustering stage k-means algorithm has been used to cluster the SOM units. Quantitative (using two categories of validity indices) and qualitative criteria were introduced to verify the results of the clustering. The different experiments developed extracted six distinct classes, which were related to typical meteorological conditions in the area.