Designing an experiment for programming education research, in which collecting and interpreting a large number of qualitative data about programmers is required, needs careful consideration in order to validate the e...
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ISBN:
(纸本)9781450314640
Designing an experiment for programming education research, in which collecting and interpreting a large number of qualitative data about programmers is required, needs careful consideration in order to validate the experiment. When it comes to finding a pattern in the programming behaviour of a specific group of programmers (e,g. novice, intermediate or expert programmers), one of the critical issues is the selection of similar participants who can be placed in one group. In this study we were interested in finding a method that could shorten the path to finding participants. Therefore, the use of clustering algorithms to group similar participants was put to test in order to investigate the effectiveness and feasibility of this approach. The clustering algorithms that were used for this study were K-means and DBSCAN. The results showed that the use of these algorithms, for the mentioned purpose, is feasible and that both algorithms can identify similar participants and place them in the same group while participants who are not similar to others, and therefore are not the correct subject of the study, are recognised. Copyright 2012 ACM.
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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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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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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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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Semi-supervised clustering uses a small amount of supervised data in the form of pairwise constraints to improve the clustering performance. However, most current methods are passive in the sense that the pairwise con...
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