Gary K. Chen, Eric Chi, John Ranola, Kenneth Lange
The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical […]
Dariusz Cieslakiewicz
During times of stock market turbulence and crises, monitoring the clustering behaviour of financial instruments allows one to better understand the behaviour of the stock market and the associated systemic risks. In the study undertaken, I apply an effective and performant approach to classify data clusters in order to better understand correlations between stocks. The […]
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Natalya Litvinenko
This problem was solved within the framework of the grant project "Solving of problems of cluster analysis with application of parallel algorithms and cloud technologies" in the Institute of Mathematics and Mathematical Modelling in Almaty. The problem of cluster analysis for the large amount of data is very important in different areas of science – […]
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Natalya Litvinenko
In this paper we solve on GPUs massive problems with large amount of data, which are not appropriate for solution with the SIMD technology. For the given problem we consider a three-level parallelization. The multithreading of CPU is used at the top level and graphic processors for massive computing. For solving problems of cluster analysis […]
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Christoffer Hirth
Quantum dots, that is, strongly confined electrons, show a variety of interesting properties. Of relevance in both experiments and various technical components, is the possibility to fine tune their electrical and optical properties. Quantum dots can be manufactured by a number of different techniques in practice, but we have in this thesis employed computer simulations […]
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Takazumi Matsumoto, Edward Hung
Outlier detection (also known as anomaly detection) is a common data mining task in which data points that lie outside expected patterns in a given dataset are identified. This is useful in areas such as fault detection, intrusion detection and in pre-processing before further analysis. There are many approaches already in use for outlier detection, […]
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Raul Ernesto Torres Carvajal
This document proposes an alternative method for the comparison of molecular electrostatic potential (MEP), based on parallel computing algorithms on graphics cards using NVIDIA CUDA platform and kernel methods for pattern recognition. The proposed solution optimizes the construction process of a particular representation of MEP, presents options for improving this representation, and offers 11 kernel […]
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Robin M. Weiss
Swarm intelligence describes the ability of groups of social animals and insects to exhibit highly organized and complex problem-solving behaviors that allow the group as a whole to accomplish tasks which are beyond the capabilities of any individual. This phenomenon found in nature is the inspiration for swarm intelligence algorithms — systems that utilize the […]
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Hiroyki Takizawa, Hiroaki Kobayashi
This paper presents an effective scheme for clustering a huge data set using a commodity programmable graphics processing unit(GPU). Due to GPUs application-specific architecture, one of the current research issues is how to bind the rendering pipeline with the data-clustering process. By taking advantage of GPUs parallel processing capability, our implementation scheme is devised to […]
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