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K-means is in fact a family of clustering algorithms with different distance functions and a variety of extension, e.g., fuzzy clustering and consensus clustering. Nevertheless, K-means-based clustering algorithms employ the similar two-phase iterative procedure including distance computation and centroids updating. Therefore, to explore the parallel implementations of this two-phase iterative procedure...
MapReduce is undoubtedly the most popular framework for large-scale processing and analysis of vast data sets in clusters of machines. To facilitate the easier use of MapReduce, SQL-like declarative languages and SQL-to-MapReduce translators have attracted increasing attentions recently. The SQL-to-MapReduce translator can automatically generate the MapReduce jobflow for each SQL query submitted by...
Community detection is a classic and very difficult task in complex network analysis. As the increasingly explosion of social media, scaling community detection methods to large networks has attracted considerable recent interests. In this paper, we propose a novel SIMPLifying and Ensembling (SIMPLE) framework for parallel community detection. It employs the random link sampling to simplify the network...
City objects recommendation based on characteristics of users, location, time and weather is a challenging issue in geographical information retrieval (GIR). In the meanwhile, city objects recommendation is a computation-intensive and data-intensive application. Cloud computing has gained significant attention in recent years to process the large volume of data. MapReduce framework is currently a...
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