Survey on Clustering on the Cloud by Using Map Reduce in Large Data Applications

M Chaitanya Kumari; P Nagendra Babu

doi:https://doi.org/10.14445/22315381/IJETT-V21P275

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Volume 21 | Number 2 | Year 2015 | Article Id. IJETT-V21P275 | DOI : https://doi.org/10.14445/22315381/IJETT-V21P275

Survey on Clustering on the Cloud by Using Map Reduce in Large Data Applications

M Chaitanya Kumari, P Nagendra Babu

Citation :

M Chaitanya Kumari, P Nagendra Babu, "Survey on Clustering on the Cloud by Using Map Reduce in Large Data Applications," International Journal of Engineering Trends and Technology (IJETT), vol. 21, no. 2, pp. 392-395, 2015. Crossref, https://doi.org/10.14445/22315381/IJETT-V21P275

Abstract

The term Clustering implies grouping of objects depends upon their similarity. In another way clustering is the process of grouping a set of objects so that objects within a group or cluster have high similarity, but comparing objects with other clusters must have high dissimilarity. In Cloud computing multiple users can access a single server to retrieve and update their data without purchasing licenses for different applications. The need of clustering on cloud is to retrieve the appropriate data because now a days we are dealing with peta bytes of data. For this reason we are using map reduce frame work which handles huge amounts of data by using two phases such as “Map” and “Reduce”. Several algorithms such as K-Means, KMedoids, CLARA, and CLARANS are used in clustering. If we use CLARA with Hadoop Map Reduce frame work cloud will be very effective and we can achieve better efficiency.

Keywords

CLARA, Map Reduce, Scientific Computing, Cloud Computing, Scheduling algorithm, K-Means.

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