This paper proposes a method based on complex networks analysis, devised to perform clustering on multidimensional datasets. In particular, the method maps the elements of the dataset in hand to a weighted network according to the similarity that holds among data. Network weights are computed by transforming the Euclidean distances measured between data according to a Gaussian model. Notably, this model depends on a parameter that controls the shape of the actual functions. Running the Gaussian transformation with different values of the parameter allows to perform multiresolution analysis, which gives important information about the number of clusters expected to be optimal or suboptimal.
Clustering datasets by complex networks analysis
Giuliano Armano and Marco Alberto Javarone
Complex Adaptive Systems Modeling 2013, 1:5 http://dx.doi.org/10.1186/2194-3206-1-5