In this paper, we present a clustering based method used to process 3D seismic data and automatically map seismic horizons in the presence of discontinuities. Our approach uses the cosine of instantaneous phase attrib...
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ISBN:
(纸本)9789462821859
In this paper, we present a clustering based method used to process 3D seismic data and automatically map seismic horizons in the presence of discontinuities. Our approach uses the cosine of instantaneous phase attributes and applies Principal Component Analysis to the original datasets of trace shapes to improve the quality of the original samples. We also propose a measurement to infer the quality of the clusters used to map the seismic horizons. Based on this measurement, we show that using the cosine of instantaneous phase attributes and PCA greatly improves the mapping of seismic horizons.
In some application contexts, data are better described by a matrix of pairwise dissimilarities rather than by a vector representation. clustering and topographic mapping algorithms have been adapted to this type of d...
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In some application contexts, data are better described by a matrix of pairwise dissimilarities rather than by a vector representation. clustering and topographic mapping algorithms have been adapted to this type of data, either via the generalized Median principle, or more recently with the so called relational approach, in which prototypes are represented by virtual linear combinations of the original observations. One drawback of those methods is their complexity, which scales as the square of the number of observations, mainly because they use dense prototype representations: each prototype is obtained as a virtual combination of all the elements of its cluster (at least). We propose in this paper to use a sparse representation of the prototypes to obtain relational algorithms with sub-quadratic complexity.
Field Programmable Gate Arrays (FPGAs) have become a popular medium for the implementation of many digital circuits. Mapping applications into FPGAs requires a set of efficient Computer-Aided Design (CAD) tools to obt...
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clustering algorithm is one of the most popular data analysis technique in machine learning to precisely evaluate the vast number of healthcare data from the body sensor networks, internet of things devices, hospitals...
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As product reviews accumulate more and more at online shopping sites, customers begin to have an increasing demand for analyzing reviews automatically. In some previous studies, clustering algorithms have been proved ...
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The paper considers the Gaussian mixtures model and the possibilities of its application for solving clustering tasks. First, the case is considered when the Gaussian mixtures model is formed in such a way that all th...
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clustering algorithms are useful whenever one needs to classify an excessive amount of information into a set of manageable and meaningful subsets. Using an analogy from vector analysis, a clustering algorithm can be ...
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ISBN:
(纸本)0819440620
clustering algorithms are useful whenever one needs to classify an excessive amount of information into a set of manageable and meaningful subsets. Using an analogy from vector analysis, a clustering algorithm can be said to divide up state space into discrete "chunks" such that each vector lies within one chunk. These vectors can best be thought of as sets of features. A canonical vector for each region of state space is chosen to represent all vectors which are located within that region. The following paper presents a survey of clustering algorithms. It pays particular attention to those algorithms that require the least amount of a priori knowledge about the domain being clustered. In the current work, an algorithm is compelling to the extent that it minimizes any assumptions about the distribution of vectors being classified.
For massive data, traditional clustering methods often require repeated iterations and calculations, which consume a lot of time and resources. Therefore, this article chooses to use big data clustering algorithms to ...
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Peer-to-Peer (p2p) networks are used by millions for searching content. Recently, clustering algorithms were shown to be useful for helping users find content in such networks. However, p2p networks often exhibit powe...
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The successful provision of context aware services entails the attainment of equilibrium between the extent of personalization desired and the user's need for privacy. Two are the major elements that play a signif...
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