Text or document clustering is a subset of a larger field of data clustering and has been one of the research hotspots in text mining. On the other hand, recent studies have shown that many real systems may be represented as complex networks with astonishing similar proprieties. In this work a document corpora is represented as a complex network of documents, in which the nodes represent the documents and the edges are weighted according to the similarities among documents. The detection of community structures in complex networks can be seen as the cluster analysis in document networks. Recently community detection algorithms based on spectral proprieties of the underlying has shown good results. The main motivation for applying those methods is that they have shown to be robust to the high dimensionality of feature space and also to the inherent data sparsity resulting from text representation in the vector space model. The aim of this paper is to present the application of the community structures algorithms for text mining. Experiments have been carried out on the document clustering problems taken from 20 newsgroup document corpora to evaluate the performance of the proposed approach.
This paper investigates the use of three external feedback connections in the development of new recurrent fuzzy models for prediction of nonlinear dynamic systems. These models are formulated by state space equations. The state transition function is a TSK recurrent fuzzy system with an external feedback connection and adaptive delayed operators, and the output function is a polynomial function of the states. The identification of the model's parameters is carried out by a canonical differential evolution algorithm. The model performance was evaluated in benchmark problems found in the literature and the results demonstrated that the aiding of external feedback enhanced the recurrent fuzzy system quality, yielding models with good performance.
Document clustering is one of the most active research topics in text mining. In this work two approaches issued from very different fields are explored for document clustering: spectral clustering and community detection in complex networks. Both approaches are based on a representation of the document collection as a graph, of which the nodes represent the documents and the edges represent the similarities between each pair of documents, such that the two approaches have many issues in common. The results of the application of these two types of techniques to benchmark text mining problems show that they are complementary and are useful for finding structure in large collections of documents
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