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Data mining and machine learning algorithms deal with large amount of data, which with the invention of cost-efficient devices has increased by massive amounts. Many algorithms of these domains are not part of real-time systems because of their computational complexity and large data on which they need to work. A lot of algorithms are being implemented on parallel processing systems like GPUs and...
Frequent itemset mining is a fundamental step in analysis of big data where correlation among the raw data in deemed necessary. In modern era the amount of data available for processing has grown exponentially, making it a stepper task for mining algorithms to provide solution in a timely manner. The software implementations are normally not efficient in handling such datasets thus focus on parallel...
Organizing data into its natural grouping based on intrinsic characteristics is the most sensible thing to do with unlabeled data. Mean shift is a non-parametric mode seeking algorithm widely used for data clustering, image segmentation and object tracking, but its use in real time applications is limited because of its high computational cost. In this paper we propose a hybrid, sequentially unfolded,...
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