• Juliana

Big data has become general for processing, storing and handling massive volumes of data. The clustering of datasets has become a interesting issue in the field of big data analytics. The K-means algorithm is a promising solution for finding similarities between entities based on distance measures with small datasets. Existing clustering algorithms require accessible solutions to manage large datasets. This study presents Advanced Clustering Algorithm (ACA) approach to cluster large datasets using MapReduce. The results of the proposed approaches show significant improvements in the efficiency of clustering in terms of execution time. Experiments conducted on standard K-means and proposed solutions show that the advanced clustering approach is both effective and efficient. Keywords: Clustering, Big data, Hadoop, MapReduce, Scalability

Keywords: Clustering, Big data, Hadoop, MapReduce, Scalability

"Clustering large datasets using Advanced K‑means method in Hadoop", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.598-602, January-2019, Available :http://www.jetir.org/papers/JETIR1901779.pdf

"Clustering large datasets using Advanced K‑means method in Hadoop", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp598-602, January-2019, Available at : http://www.jetir.org/papers/JETIR1901779.pdf