Complex networks are used in different domains to model specific structures or behaviors 2010. Relevant examples are the Web, biological neural networks, and social networks 2002,2004,2003. Community detection is one of the most important processes in complex network analysis, aimed at identifying groups of highly mutually interconnected nodes, called communities 2004, in a relational space. From a complex network perspective, a community is identified after modeling any given dataset as graph. For instance, a social network inherently contains communities of people linked by some (typically binary) relations –e.g., friendship, sports, hobbies, movies, books, or religion. On the other hand, from a machine learning perspective, a community can be thought of as a cluster. In this case, elements of the domain are usually described by a set of features, or properties, which permit to assign each instance a point in a multidimensional space. The concept of similarity is prominent here, as clusters are typically identified by focusing on common properties (e.g., age, employment, health records).

The problem of clustering multidimensional datasets without a priori knowledge about them is still open in the machine learning community (see, for example, 2010,2001,1998). Although complex networks are apparently more suited to deal with relations rather than properties, nothing prevents from representing a dataset as complex network. In fact, the idea of viewing datasets as networks of data has already been developed in previous works. Just to cite few, Heimo et al. 2008 studied the problem of multiresolution module detection in dense weighted networks, using a weighted version of the *q*‐state Potts method. Mucha et al. 2010 developed a generalized framework to study community structures of arbitrary multislice networks. Toivonen et al. 2012 used network methods in analyzing similarity data with the aim to study Finnish emotion concepts. Furthermore, a similar approach has been developed by Gudkov et al. 2008, who devised and implemented a method for detecting communities and hierarchical substructures in complex networks. The method represents nodes as point masses in an *N*−1dimensional space and uses a linear model to account for mutual interactions.

The motivation for representing a dataset as graph lies in the fact that very effective algorithms exist on the complex network side to perform community detection. Hence, these algorithms could be used to perform clustering once the given dataset has been given a graph‐based representation. Following this insight, in this paper we propose a method for clustering multidimensional datasets in which they are first mapped to weighted networks and then community detection is enforced to identify relevant clusters. A Gaussian transformation is used to turn distances of the original (i.e. feature‐based) space to link weights of the complex networks side. As the underlying Gaussian model is parametric, the possibility to run Gaussian transformations multiple times (while varying the parameter) is exploited to perform multiresolution analysis, aimed at identifying the optimal or suboptimal number of clusters.

The proposed method, called *DAN* (standing for Datasets as Networks), makes a step forward in the direction of investigating the possibility of using complex network analysis as a proper machine learning tool. The remainder of the paper is structured as follows: Section Methods describes how to model a dataset as complex network and gives details about multiresolution analysis. For the sake of readability, the section briefly recalls also some informative notion about the adopted community detection algorithm. Section Results and discussion illustrates the experiments and analyzes the corresponding results. The section recalls also some relevant notions of clustering, including two well‐known algorithms, used therein for the sake of comparison. Conclusions (i.e. Section Conclusions) end the paper.