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Clustering with Apache Hadoop

Authors:

S. Nair,

J. MehtaAuthors Info & Claims

ICWET '11: Proceedings of the International Conference & Workshop on Emerging Trends in Technology

Pages 505 - 509

https://doi.org/10.1145/1980022.1980131

Published: 25 February 2011 Publication History

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Abstract

The self-organizing map (SOM) is an unsupervised neural network which projects high-dimensional data onto a low-dimensional grid and visually reveals the topological order of the original data. Thus, SOM is an excellent tool in the exploratory phase of data mining. Self-organizing maps have been successfully applied to many fields, including engineering and business domains. Experimental results on census database illustrate the results of clustering.

The paper proposes to improve the performance of clustering by the latest approach of cloud computing.

The approach focuses on Hadoop that provides a Java-based software framework to distribute processing over a cluster of processors by providing a open source implementation of MapReduce, a powerful tool designed for the detailed analysis and transformation of very large data sets.

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Cited By

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Tripathy BSeetha HMurty M(2018)Uncertainty-Based Clustering Algorithms for Large Data SetsModern Technologies for Big Data Classification and Clustering10.4018/978-1-5225-2805-0.ch001(1-33)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-2805-0.ch001
况俞(2016)Big Data Research Analysis Based on BibliometricsHans Journal of Data Mining10.12677/HJDM.2016.6301506:03(125-137)Online publication date: 2016
https://doi.org/10.12677/HJDM.2016.63015
Ludwig S(2015)MapReduce-based fuzzy c-means clustering algorithm: implementation and scalabilityInternational Journal of Machine Learning and Cybernetics10.1007/s13042-015-0367-06:6(923-934)Online publication date: 29-Apr-2015
https://doi.org/10.1007/s13042-015-0367-0
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Index Terms

Clustering with Apache Hadoop
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

ICWET '11: Proceedings of the International Conference & Workshop on Emerging Trends in Technology

February 2011

1385 pages

ISBN:9781450304498

DOI:10.1145/1980022

Program Chair:
B. K. Mishra

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

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Published: 25 February 2011

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Thakur College Of Engg. & Tech

ICWET '11: International Conference & Workshop on Emerging Trends in Technology

February 25 - 26, 2011

Maharashtra, Mumbai, India

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Tripathy BSeetha HMurty M(2018)Uncertainty-Based Clustering Algorithms for Large Data SetsModern Technologies for Big Data Classification and Clustering10.4018/978-1-5225-2805-0.ch001(1-33)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-2805-0.ch001
况俞(2016)Big Data Research Analysis Based on BibliometricsHans Journal of Data Mining10.12677/HJDM.2016.6301506:03(125-137)Online publication date: 2016
https://doi.org/10.12677/HJDM.2016.63015
Ludwig S(2015)MapReduce-based fuzzy c-means clustering algorithm: implementation and scalabilityInternational Journal of Machine Learning and Cybernetics10.1007/s13042-015-0367-06:6(923-934)Online publication date: 29-Apr-2015
https://doi.org/10.1007/s13042-015-0367-0
Al-Madi NAljarah ILudwig S(2014)Parallel glowworm swarm optimization clustering algorithm based on MapReduce2014 IEEE Symposium on Swarm Intelligence10.1109/SIS.2014.7011794(1-8)Online publication date: Dec-2014
https://doi.org/10.1109/SIS.2014.7011794
Ma MWang PChu C(2013)Data Management for Internet of ThingsProceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing10.1109/GreenCom-iThings-CPSCom.2013.199(1144-1151)Online publication date: 20-Aug-2013
https://dl.acm.org/doi/10.1109/GreenCom-iThings-CPSCom.2013.199

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G-Hadoop: MapReduce across distributed data centers for data-intensive computing

Research of Cloud Computing Based on the Hadoop Platform

'Big data', Hadoop and cloud computing in genomics

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