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  1. Proceedings of the Second Nordic Symposium on Cloud Computing & Internet Technologies (NordiCloud '13)
  2. Clustering on the cloud: reducing CLARA to MapReduce
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NordiCloud 2013: a Nordic and Baltic gathering for supporting cloud computing and internet technologies
Cloud brokerage: a key enabler for widespread cloud usage
CLOUDs: hype, problems and opportunities
Secure cloud storage for remote mobile data collection
A secure OSGi environment for untrusted web applications
Distributed testing of cloud computing applications using the TTCN-3-based Jata test framework
Developing migratable multicloud applications based on MDE and adaptation techniques
Managing multi-cloud systems with CloudMF
A vision for a stochastic reasoner for autonomic cloud deployment
Real-time scheduling in cloud-based virtualized software systems
Engineering virtualized services
Clustering on the cloud: reducing CLARA to MapReduce
Perspectives and reflections on cloud computing and internet technologies from NordiCloud 2012

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Clustering on the cloud: reducing CLARA to MapReduce

Content Provider ACM Digital Library
Author Srirama, Satish Narayana Jakovits, Pelle
Abstract Cloud computing, with its promise of virtually limitless resources, seems to suit well in solving resource intensive problems from machine learning and data mining domains, by allowing to scale any distributed data mining or machine learning application with little difficulty. However, to be able to run these applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like Hadoop MapReduce. It offers both automatic parallelization and fault tolerance on the cloud commodity hardware. However, it is not trivial to adapt complex algorithms to MapReduce model, as often it is more suited for simple and embarrassingly parallel algorithms. Yet, there are some types of more complex algorithms that are suitable for MapReduce and in this work we look at one such algorithm, Clustering LARge Applications (CLARA), which can be used for clustering extra large number of objects. The paper describes how CLARA is reduced to MapReduce model along with a detailed analysis in the Hadoop MapReduce implementation. The paper also provides a case study where the algorithm is successfully applied in clustering pen-based recognition of handwritten digits data set.
Starting Page 64
Ending Page 71
Page Count 8
File Format PDF
ISBN 9781450323079
DOI 10.1145/2513534.2513546
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-09-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Cloud computing K-medoid clustering Clara Classification Mapreduce
Content Type Text
Resource Type Article
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