Hanu Hooda, Rainu Nanda
Big Data poses a very great computational challenge for programmers as well as machines as a lot of number crunching is to be done.Due to recent development in the shared memory inexpensive architecture like Graphics Processing Units (GPU), an alternative has emerged. In this paper, we target at decreasing runtime for k-Means, which is one […]
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Seth Hall
Mobile devices offer many new avenues for computer vision and in particular mobile augmented reality applications that have not been feasible with desktop computers. The motivation for this research is to improve mobile augmented reality applications so that natural features, instead of fiducial markers or pure location knowledge, can be used as anchor points for […]
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Ursula Reiterer
Clustering is a basic task in exploratory data analysis. It is used to partition elements of a set into disjoint groups, so-called clusters, such that elements within a group are similar to each other, but dissimilar to elements of other groups. Several clustering algorithms exist, which can be applied depending on the type of dataset […]
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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 […]
Takazumi Matsumoto, Edward Hung, Man Lung Yiu
Outlier detection, also known as anomaly detection, is a common data mining task in identifying data points that are outside expected patterns in a given dataset. It has useful applications such as network intrusion, system faults, and fraudulent activity. In addition, real world data are uncertain in nature and they may be represented as uncertain […]
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S.A. Arul Shalom, Manoranjan Dash
Graphics Processing Units (GPU) in today’s desktops can well be thought of as a high performance parallel processor. Traditionally, parallel computing is the usage of multiple computing resources to execute computational problems simultaneously. Such computations are possible using multi-core CPUs or computers with multiple CPUs or by using a network of computers in parallel. Today’s […]
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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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Jianwei Cao, Qingkui Chen, Songlin Zhuang
With Extensive use of wireless sensor network is drawing increasing attention to the research on data-driven processing but it is a challenge to construct a system of concurrent processing for large-scale data streams (LCDS), a typical model of data-driven process. As Graphic Processing Unit (GPU) has good characteristics of SPMD (Single Program Multiple Data) while […]
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Vidya Dhamdhere, Rahul G. Ghudji
K-Means is the most popular clustering algorithm in data mining. The size of various data sets has increased tremendously day by day. Due to recent development in the shared memory inexpensive architecture like Graphics Processing Units (GPU). The general – purpose applications are implemented on GPU using Compute Unified Device Architecture (CUDA). Cost effectiveness of […]
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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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