Survey on Clustering on the Cloud by Using Map Reduce in Large Data Applications
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : M Chaitanya Kumari, P Nagendra Babu
|DOI : 10.14445/22315381/IJETT-V21P275|
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), V21(8),392-395 March 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
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.
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CLARA, Map Reduce, Scientific Computing, Cloud Computing, Scheduling algorithm, K-Means.