Vol 3 , Issue 1 , January - March 2015 | Pages: 52-56 | Research Paper
Received: February 04, 2015 | Revised: February 20, 2015 | Accepted: February 28, 2015 | Published Online: March 15, 2015
Author Details
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Clustering big data using data mining algorithms is a modern approach, used in various science and medical fields. k-means clustering algorithm is a good approach for clustering, but choosing initial centers and provides less accuracy guarantees. The enhanced k-means approach called π-means++ chooses one center uniformly at random provides better functionality, but fails to handle data of larger volume in distributed environment. The mapreduce π-means++ method handles k-means++ algorithm by enhancing it in mapper and reducer phases, also reduces the no of iterations required to obtain π centers. in which the π-means++ initialization algorithm is executed in the mapper phase and the weighted π-means++ initialization algorithm is run in the reducer phase. it reduces huge amount of communication and i/o costs. the proposed mapreduce π-means++ method obtains (πΌ2) approximation to the optimal solution of π-means.
Keywords
VANET; MANET; ZRP; LAR; IDM; Vanet Mobi Sim; ns2