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A Novel Clustering Approach Using Hadoop Distributed Environment

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Computational Intelligence Techniques for Comparative Genomics

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSFOMEBI))

Abstract

Nowadays, information retrieval plays a vital role by allowing users to retrieve documents of their interest based on relevance score. Such systems can be implemented either in distributed systems or parallel systems to achieve high throughput. If such kind of framework is deployed in a cloud, grouping of relevant documents is essential to retrieve documents of interest. Hence, an efficient and scalable clustering is required to process huge volume of documents. To handle huge documents and to provide scalability while processing Apache Hadoop is efficient with its powerful feature map reduce. Hence, in this paper, a novel approach is proposed that is capable of clustering bulk data with high throughput. This paper also demonstrates the need of parallel caching approach for obtaining effective results.

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References

  1. Lynch C (2008) Big data: how do your data grow? Nature 455(7209):28–29

    Article  Google Scholar 

  2. Ye K et al (2012) vHadoop: a scalable hadoop virtual cluster platform for mapreduce-based parallel machine learning with performance consideration. In: IEEE international conference on cluster computing workshops, pp 152–160

    Google Scholar 

  3. Dean J et al (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  4. White T (2010) Hadoop: the definitive guide. Yahoo Press

    Google Scholar 

  5. Vadaparthi Nagesh et al (2011) Segmentation of brain MR images based on finite skew gaussian mixture model with fuzzy C-Means clustering and -EM algorithm. Int J Comput Appl 28(10):18–26

    Google Scholar 

  6. Sabena S et al (2011) Image retrieval using canopy and improved K mean clustering. In: International conference on emerging technology trends (ICETT) 2011, pp 15–19

    Google Scholar 

  7. McCallum A et al (2011) Efficient clustering of high-dimensional data sets with application to reference matching. White papers

    Google Scholar 

  8. Bradley PS et al (1998) Scaling clustering algorithms to large databases. In: Proceeding of 4th international conference on knowledge discovery and data mining (KDD-98). AAAI Press, Menlo Park

    Google Scholar 

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Correspondence to Nagesh Vadaparthi .

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Vadaparthi, N., Srinivas Rao, P., Srinivas, Y., Athmaja, M. (2015). A Novel Clustering Approach Using Hadoop Distributed Environment. In: Muppalaneni, N., Gunjan, V. (eds) Computational Intelligence Techniques for Comparative Genomics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-287-338-5_9

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  • DOI: https://doi.org/10.1007/978-981-287-338-5_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-337-8

  • Online ISBN: 978-981-287-338-5

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