Bigdata implementation of apriori algorithm for handling voluminous data-sets

  • Authors

    • M. Nagalakshmi
    • I. Surya Prabha
    • K. Anil
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.5.9149
  • Frequent Itemset, Distributed Computing, Hadoop, Apriori, Distributed data processing

  • Abstract

    Apriori is one all instructed the key algorithms to come again up with frequent itemsets. Analysing frequent itemset could be an critical step in analysing based info and recognize association dating among matters. This stands as degree standard basis to supervised gaining knowledge of, that encompasses classifier and feature extraction strategies. making use of this system is vital to grasp the behaviour of structured data. maximum of the dependent information in scientific domain square measure voluminous. method such moderately info desires country of the artwork computing machines. setting up region such degree infrastructure is high priced. so a allotted environment admire a clustered setup is hired for grappling such situations. Apache Hadoop distribution is one all advised the cluster frameworks in allotted environment that enables by means of distributing voluminous data across style of nodes most of the framework. This paper specializes in map/reduce trend and implementation of Apriori formula for dependent info analysis.

  • References

    1. [1] Souptik Datta, Kanishka Bhaduri, Chris Giannella, Ran Wolff, and Hillol Kargupta, Distributed DataMining in Peer-to-Peer Networks, Universityof Maryland, Baltimore County, Baltimore, MD, USA,

      [2] Journal IEEE Internet Computing archive Volume 10 Issue 4, Pages 18 - 26, July 2006.

      [3] Ning Chen, Nuno C. Marques, and Narasimha Bolloju, A Web Service based approach for data mining in distributed environments, Department of Information Systems, City University of Hong Kong, 2005.

      [4] Mafruz Zaman A shrafi, David Taniar, and Kate A. Smith, A Data Mining Architecture for Distributed Environments, pages 27-34, Springer-Verlag London, UK, 2007.

      [5] Grigorios Tsoumakas and Ioannis Vlahavas, Distributed Data Mining of Large Classifier Ensembles, SETN-2008, Thessaloniki, Greece, Proceedings, Companion Volume, pp. 249-256, 11-12 April 2008.

      [6] Vuda Sreenivasa Rao, Multi Agent-Based Distributed Data Mining: An Over View, International Journal of Reviews in computing, pages 83-92,2009.

      [7] P.Kamakshi, A.Vinaya Babu, Preserving Privacy and Sharing the Datain Distributed Environment using Cryptographic Technique on Perturbed data, Journal Of Computing, Volume 2, Issue 4, ISSN 21519617, April2010.

      [8] Feng LI, Jin MA, Jian-hua LI, Distributed anonymous data perturbation method for privacy-preserving data mining, Journal of Zhejiang University SCIENCE A ISSN 1862-1775, pages 952-963, 2008.

      [9] Goswami D.N. et. al., An Algorithm for Frequent Pattern Mining Based On Apriori (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 04, 942-947, 2010.

      [10] Marcin Gorawski and Pawel Jureczek, Using Apriori-like Algorithms for Spatio-Temporal Pattern Queries, Silesian University of Technology, Institute of Computer Science, Akademicka 16, Poland, 2010.

      [11] Cheng-Tao Chu et. al., Map-Reduce for Machine Learning on Multicore, CS Department, Stanford University, Stanford, CA, 2006.

      [12] Navraj Chohanet. al., See Spot Run: Using Spot Instances for Map-Reduce Workflows, Computer Science Department, University of California,2005.

      [13] Rajesh, M., and J. M. Gnanasekar. "Congestion control in heterogeneous wireless ad hoc network using FRCC." Australian Journal of Basic and Applied Sciences 9.7 (2015): 698-702.

      [14] S.V.Manikanthan and V.Rama“Optimal Performance Of Key Predistribution Protocol In Wireless Sensor Networks†International Innovative Research Journal of Engineering and Technology ,ISSN NO: 2456-1983,Vol-2,Issue –Special –March 2017.

      [15] T. Padmapriya and V. Saminadan, “Inter-cell Load Balancing Technique for Multi- class Traffic in MIMO - LTE - A Networksâ€, International Conference on Advanced Computer Science and Information Technology , Singapore, vol.3, no.8, July 2015.

  • Downloads

  • How to Cite

    Nagalakshmi, M., Surya Prabha, I., & Anil, K. (2017). Bigdata implementation of apriori algorithm for handling voluminous data-sets. International Journal of Engineering & Technology, 7(1.5), 217-220. https://doi.org/10.14419/ijet.v7i1.5.9149

    Received date: 2018-01-11

    Accepted date: 2018-01-11

    Published date: 2017-12-31