In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The method builds upon a novel density based clustering scheme that explicitly takes into account earthquake's magnitu...
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In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The method builds upon a novel density based clustering scheme that explicitly takes into account earthquake's magnitude during the density estimation. The new density based clustering algorithm considers both time and spatial information during cluster formation. Therefore clusters lie in a spatio-temporal space. A hierarchical agglomerative clustering algorithm acts upon the identified clusters after dropping the time information in order to come up only with the spatial description of seismic events. The approach is demonstrated using data from the vicinity of the Hellenic seismic arc in order to enable its comparison with some of the state-of-the-art distinct seismic region identification methodologies. The presented results indicate that the combination of the two clustering stages could be potentially used for an automatic definition of major seismic sources. (C) 2013 Elsevier Ltd. All rights reserved.
One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to compute...
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One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret. Unfortunately, it is well known, however, that many of the classic agglomerative clustering algorithms are not robust to noise. In this paper we propose and analyze a new robust algorithm for bottom-up agglomerative clustering. We show that our algorithm can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also show how to adapt our algorithm to the inductive setting where our given data is only a small random sample of the entire data set. Experimental evaluations on synthetic and real world data sets show that our algorithm achieves better performance than other hierarchical algorithms in the presence of noise.
Following the general principles of the logical analysis of data methodology, originally developed for the case of binary data. we define a similar approach for the analysis of numerical data. The central concepts of ...
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Following the general principles of the logical analysis of data methodology, originally developed for the case of binary data. we define a similar approach for the analysis of numerical data. The central concepts of this methodology are those of homogeneous boxes and of saturated systems of homogeneous boxes. The box-clustering heuristic described in this paper is efficient and was applied successfully for the analysis of datasets concerning breast tumors, oil exploration and diabetes. (C) 2004 Elsevier B.V. All rights reserved.
This paper presents new robust clustering algorithms, which significantly improve upon the noise and initialization sensitivity of traditional mixture decomposition algorithms, and simplify the determination of the op...
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This paper presents new robust clustering algorithms, which significantly improve upon the noise and initialization sensitivity of traditional mixture decomposition algorithms, and simplify the determination of the optimal number of clusters in the data set. The algorithms implement maximum likelihood mixture decomposition of multivariate t-distributions, a robust parametric extension of gaussian mixture decomposition. We achieve improved convergence capability relative to the expectation-maximization (EM) approach by deriving deterministic annealing EM (DAEM) algorithms for this mixture model and turning them into agglomerative algorithms (going through a monotonically decreasing number of components), an approach we term deterministic agglomeration EM (DAGEM). Two versions are derived, based on two variants of DAEM for mixture models. Simulation studies demonstrate the algorithms' performance for mixtures with isotropic and non-isotropic covariances in two and 10 dimensions with known or unknown levels of outlier contamination. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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