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2012 IEEE 12th International Conference on Data Mining Workshops
Scalable Clustering Using PACT Programming Model
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
Recent spurt in research related to scalability of data mining algorithms can be attributed to advances in cloud computing technology, which enables data-intensive applications in distributed environment. Map-Reduce has been the most popular programming paradigm for developing applications in large scale distributed environments. In this paper we present design of a scalable clustering algorithm 'Exclusive and Complete Clustering using PACT Programming model' (ExCC-P) for recently developed Stratosphere system for cloud computing environment. This system supports novel model for programming in large scale distributed environments. Based on the concept of Parallelization Contracts, the PACT programming model is a generalization of Map-Reduce paradigm. PACT programs are complied by a PACT compiler and executed by Nephele execution engine of Stratosphere after optimizing data-flow graphs. The algorithm ExCC-P is proposed as a solution for incremental clustering of unbounded massive data sets, to be executed in Stratosphere environment. The algorithm discretizes data space into a conceptual grid and processes data in batches. After a batch is processed, the algorithm applies connected component analysis on the grid to deliver arbitrarily shaped clusters. Limited experimentation on this under-development system (Stratosphere) validated PACT programming model for the proposed algorithm.
Index Terms:
Contracts,Clustering algorithms,Algorithm design and analysis,Programming,Terrestrial atmosphere,Computational modeling,Parallel processing,PACT,Scalable,Incremental clustering,Grid synopsis,Cloud computing,MapReduce
Citation:
Sharanjit Kaur, Dhriti Khanna, Tripti Gupta, Vasudha Bhatnagar, "Scalable Clustering Using PACT Programming Model," icdmw, pp.424-430, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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