D. Hendricks, D. Cieslakiewicz, D. Wilcox, T. Gebbie
During times of stock market turbulence, monitoring the intraday clustering behaviour of financial instruments allows one to better understand market characteristics and systemic risks. While genetic algorithms provide a versatile methodology for identifying such clusters, serial implementations are computationally intensive and can take a long time to converge to the global optimum. We implement a […]
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S. Yang, J. Dong, B. Yuan
ISODATA is a well-known clustering algorithm based on the nearest neighbor rule, which has been widely used in various areas. It employs a heuristic strategy allowing the clusters to split and merge as appropriate. However, since the volume of the data to be clustered in the real world is growing continuously, the efficiency of the […]
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Vijal D. Patel, Sumitra Menaria
In today’s digital world, Data sets are increasing exponentially. Statistical analysis using clustering in various scientific and engineering applications become very challenging issue for such large data set. Clustering on huge data set and its performance are two major factors demand for optimization. Parallelization is well-known approach to optimize performance. It has been observed from […]
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Alessio Magro
Since the discovery of RRATs, interest in single pulse radio searches has increased dramatically. Due to the large data volumes generated by these searches, especially in planned surveys for future radio telescopes, such searches have to be conducted in real-time. This has led to the development of a multitude of search techniques and real-time pipeline […]
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Jiri Skala
This thesis deals with manipulating huge geometric data in the field of computer graphics. The proposed approach uses a data stream technique to allow processing gigantic datasets that by far exceed the size of the main memory. The amount of data is hierarchically reduced by clustering and replacing each cluster by a representative. The input […]
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Pinghao Li, Xiaoqian Jiang, Shuang Wang, Jihoon Kim, Hongkai Xiong, Lucila Ohno-Machado
BACKGROUND AND OBJECTIVE: Short-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data. METHODS: We developed Hierarchical mUlti-reference Genome cOmpression (HUGO), […]
Nam-Luc Tran, Quentin Dugauthier, Sabri Skhiri
In the context of processing high volumes of data, the recent developments have led to numerous models and frameworks of distributed processing running on clusters of commodity hardware. On the other side, the Graphics Processing Unit (GPU) has seen much enthusiastic development as a device for general-purpose intensive parallel computation. In this paper we propose […]
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V.Saveetha, S.Sophia, V.Anusha Sowbarnika
Clustering, as a process of partitioning data elements with similar properties, is an essential task in many application areas. Due to technological advances, the amount as well as the dimensionality of data sets in general is steadily growing. Graphics Processing Units in today’s desktops can be thought of as a high performance parallel processor. As […]
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Ivan Komarov, Ali Dashti, Roshan D'Souza
In this paper we describe a new brute force algorithm for building the k-Nearest Neighbor Graph (k-NNG). The k-NNG algorithm has many applications in areas such as machine learning, bioinformatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data the brute force search is the best […]
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Charl van Deventer, Willem A. Clarke, Scott Hazelhurst
In this dissertation we had the aim of utilizing GPU technology in order to optimize and improve on the problem of EST clustering. Extensive research on this cross-disciplinary approach was required before even considering such an approach. It was found that though this line of research has not received significant attention, there are significant gains […]
Bastiaan Onne Fagginger Auer
We consider sequential algorithms for hypergraph partitioning and GPU (i.e., fine-grained shared-memory parallel) algorithms for graph partitioning and clustering. Our investigation into sequential hypergraph partitioning is concerned with the efficient construction of high-quality matchings for hypergraph coarsening and optimisation with respect to general hypergraph partitioning quality metrics. We introduce the l*(l-1)-metric which exactly measures the […]
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Jianqiang Dong, Fei Wang, Bo Yuan
In this big data era, the capability of mining and analyzing large scale datasets is imperative. As data are becoming more abundant than ever before, data driven methods are playing a critical role in areas such as decision support and business intelligence. In this paper, we demonstrate how state-of-the-art GPUs and the Dynamic Parallelism feature […]
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