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The problem of local community detection refers to the identification of a community starting from a query node and using limited information about the network structure. Existing methods for solving this problem however are not designed to deal with multilayer network models, which are becoming pervasive in many fields of science. In this work, we present the first method for local community detection...
Projective Clustering Ensembles (PCE) has recently been formulated to solve the problem of deriving a robust projective consensus clustering from an ensemble of projective clustering solutions. PCE is formalized as an optimization problem with either a two-objective or a single-objective function, depending on whether the object-based and the feature-based representations of the clusters in the ensemble...
Recent advances in data clustering concern clustering ensembles and projective clustering methods, each addressing different issues in clustering problems. In this paper, we consider for the first time the projective clustering ensemble (PCE) problem, whose main goal is to derive a proper projective consensus partition from an ensemble of projective clustering solutions. We formalize PCE as an optimization...
This paper presents a distributed collaborative approach to XML document clustering. According to a previous study, XML documents are mapped to a transactional domain, based on a data representation model which exploits the notion of XML tree tuple. This XML transactional model is well-suited to the identification of semantically cohesive substructures from XML documents, according to structure as...
Handling microarray data is particularly challenging mainly due to the high dimensionality of such data, which demands for computer-aided methods, and to the intrinsic difficulty of devising notions of proximity between spots of array traps. In this paper, we propose a new approach to modeling the probe-level uncertainty in microarray data that allows for a more expressive representation of the data...
In recent years there has been a growing interest in clustering uncertain data. In contrast to traditional, "sharp" data representation models, uncertain data objects can be represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In this context, the focus has been mainly on partitional and density-based approaches, whereas hierarchical clustering...
Preprocessing mass spectrometry (MS) data has been recognized as a crucial preliminary phase in order to perform data management and knowledge discovery tasks on mass spectra. The huge dimensionality and heterogeneity of MS data make mandatory the use of tools that are able to guide the user in the MS preprocessing task. However, most MS preprocessing tools are typically designed to perform only some...
This paper presents a methodology to mine spectra data based on time-series analysis. MALDI-TOF spectra are modelled as time series using a compact yet feature-rich representation scheme. Experiments show that classifying mass spectrometry series is effective and can be useful for identifying peaks in spectra that can be associated to discriminant proteins.
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