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2013 IEEE 5th International Conference on Cloud Computing Technology and Science (2013)
Bristol, United Kingdom United Kingdom
Dec. 2, 2013 to Dec. 5, 2013
pp: 13-18
The accelerated evolution and explosion of the Internet and social media is generating voluminous quantities of data (on zettabyte scales). Paramount amongst the desires to manipulate and extract actionable intelligence from vast big data volumes is the need for scalable, performance-conscious analytics algorithms. To directly address this need, we propose a novel MapReduce implementation of the exemplar-based clustering algorithm known as Affinity Propagation. Our parallelization strategy extends to the multilevel Hierarchical Affinity Propagation algorithm and enables tiered aggregation of unstructured data with minimal free parameters, in principle requiring only a similarity measure between data points. We detail the linear run-time complexity of our approach, overcoming the limiting quadratic complexity of the original algorithm. Experimental validation of our clustering methodology on a variety of synthetic and real data sets (e.g. images and point data) demonstrates our competitiveness against other state-of-the-art MapReduce clustering techniques.
Vectors, Clustering algorithms, Tensile stress, Runtime, Information management, Data handling, Data storage systems

D. M. Rose, J. M. Rouly, R. Haber, N. Mijatovic and A. M. Peter, "Parallel Hierarchical Affinity Propagation with MapReduce," 2013 IEEE 5th International Conference on Cloud Computing Technology and Science(CLOUDCOM), Bristol, United Kingdom United Kingdom, 2013, pp. 13-18.
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