Abstract
Big Data is the term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. Clustering is an essential tool for clustering Big Data. Multi-machine clustering technique is one of the very efficient methods used in the Big Data to mine and analyse the data for insights. K-Means partition-based clustering algorithm is one of the clustering algorithm used to cluster Big Data. One of the main disadvantage of K-Means clustering algorithms is the deficiency in randomly identifying the K number of clusters and centroids. This results in more number of iterations and increased execution times to arrive at the optimal centroid. Sorting-based K-Means clustering algorithm (SBKMA) using multi-machine technique is another method for analysing Big Data. In this method, the data is sorted first using Hadoop MapReduce and mean is taken as centroids. This paper proposes a new algorithm called as SBKMEDA: Sorting-based K-Median clustering algorithm using multi-machine technique for Big Data to sort the data and replace median with mean as centroid for better accuracy and speed in forming the cluster.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jane, M., George Dharma Prakash Raj, E.: SBKMA: sorting based K-Means clustering algorithm using multi machine technique for Big Data. Int. J. Control Theory Appl. 8, 2105–2110 (2015)
Vrinda, Patil, S.: Efficient clustering of data using improved K-Means algorithm—a review. Imp. J. Interdiscip. Res. 2(1) (2016)
Patil, Y.S., Vaidya, M.B.: K-Means clustering with MapReduce technique. Int. J. Adv. Res. Comput. Commun. Eng. (2015)
Baswade, A.M., Nalwade, P.S.: Selection of initial centroids for K-Means Algorithm. IJCSMC 2(7), 161–164 (2013)
Vishnupriya, N., Sagayaraj Francis, F.: Data clustering using MapReduce for multidimensional datasets. Int. Adv. Res. J. Sci. Eng. Technol. (2015)
Gandhi, G., Srivastava, R.: Review paper: a comparative study on partitioning techniques of clustering algorithms. Int. J. Comput. Appl. (0975-8887) 87(9) (2014)
Bobade, V.B.: Survey paper on Big Data and Hadoop. Int. Res. J. Eng. Technol. (IRJET) 03(01) (2016)
Rauf, A., Sheeba, Mahfooz, S., Khusro, S., Javed, H.: Enhanced K-Mean clustering algorithm to reduce number of iterations and time complexity. Middle-East J. Sci. Res. 12(7), 959–963 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mahima Jane, E., George Dharma Prakash Raj, E. (2018). SBKMEDA: Sorting-Based K-Median Clustering Algorithm Using Multi-Machine Technique for Big Data. In: Rajsingh, E., Veerasamy, J., Alavi, A., Peter, J. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 645. Springer, Singapore. https://doi.org/10.1007/978-981-10-7200-0_19
Download citation
DOI: https://doi.org/10.1007/978-981-10-7200-0_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7199-7
Online ISBN: 978-981-10-7200-0
eBook Packages: EngineeringEngineering (R0)