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To improve the searching performance to find better initial cluster centers and the calculating performance to process massive data in high dimensions, for PSO K-means, a brand new hybrid data clustering algorithm named Parallelization of OBL based PSO K-means Algorithm with the OpenCL Architecture (POPK) is introduced in this paper. In POPK, Opposition-based Learning (OBL) is applied to improve the...
The Embarrassingly Parallel (EP) algorithm which is typical of many Monte Carlo applications provides an estimate of the upper achievable limits for double precision performance of parallel supercomputers. Recently, Intel released Many Integrated Core (MIC) architecture as a many-core co-processor. MIC often offers more than 50 cores each of which can run four hardware threads as well as 512-bit vector...
Different from previous works that focus on the iterative clustering algorithm, a dual-stage hardware architecture that supports two kinds of moving averages for the on-line clustering algorithm is proposed. The architecture includes a set of memories that operates in ping-pong mode, so that distance computations and centroid updating can be processed in pipeline. The high-throughput parallel divider...
Essential difference between topic detection and text clustering is distribution of news corpus and time characteristics of news corpus. So we should study topic detection according to the news corpus, and it is necessary for news corpus to be in-depth and extensive research. Vector space model (VSM) is one of the most simple and effective topics representation model. And K-means is a well-known and...
For accelerating the training speed of support vector machines (SVM), a novel ldquomulti-trifurcate cascade (MTC)rdquo architecture was proposed in this paper, which held the advantages of fast feedback, high utilization rate of nodes, and more feedback support vectors. Then, a parallel algorithm for training SVM was designed based on the MTC architecture, and it was proven to converge to the optimal...
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