NDLI: Clustering on the cloud: reducing CLARA to MapReduce

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Engineering virtualized services

Clustering on the cloud: reducing CLARA to MapReduce

Perspectives and reflections on cloud computing and internet technologies from NordiCloud 2012

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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