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Large-scale Clustering for Big Data Analytics: A MapReduce Implementation of Hierarchical Anity Propagation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rose, Dillon Mark Rouly, Jean Michel Haber, Rana Mijatovic, Nenad Peter, Adrian M. |
| Copyright Year | 2013 |
| Abstract | 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 Anity Propagation. Our parallelization strategy extends to the multilevel Hierarchical Anity 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 (Reuters articles, medical data, etc.) demonstrates our competitiveness against other state-of-the-art MapReduce clustering techniques. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.amalthea-reu.org/pubs/amalthea_tr_2013_04.pdf |
| Alternate Webpage(s) | https://michel.rouly.net/static/projects/hap-with-map/docs/technical-report.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
