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Classification of microarray data has always been a challenging task due to the enormous number of genes. Finding a small, closely related gene set to accurately classify disease cells is an important research problem. Integrating biological knowledge into genomic analysis to help to improve the interpretation of the results is an effective approach. In this paper, affinity propagation (AP) clustering...
A latency-hiding algorithm for the parallelization of large scale agent-based model simulations (ABMS) on parallel/distributed computing platform is proposed. The key idea of this algorithm is using redundant computations to hide communication latencies. An analytical model for this algorithm is presented to tell how to select R value to reach the best speedup. Compared to B+2R algorithm [1], theoretical...
Clustering is one of the most popular methods for data analysis, which is prevalent in many disciplines such as image segmentation, bioinformatics, pattern recognition and statistics etc. The most popular and simplest clustering algorithm is K-means because of its easy implementation, simplicity, efficiency and empirical success. However, the real-world applications produce huge volumes of data, thus,...
Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first intuition about their data by identifying meaningful groups among the data objects. K-means is one of the most famous clustering algorithms. Its simplicity and speed allow it to run on large data sets. However,...
In the field of data mining, clustering is one of the important methods. K-Means is a typical distance-based clustering algorithm; 2-tier clustering should implement scalable clustering by means of dividing, sampling and knowledge integrating. Among those tools of distributed processing, Map-Reduce has been widely embraced by both academia and industry. Hadoop is an open-source parallel and distributed...
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