Abstract
Increase in data volume and need for analytics has led towards innovation of big data. To speed up the query responses models like NoSQL has emerged. Virtualized platforms using commodity hardware and implementing Hadoop on it helps small and midsized companies use cloud environment. This will help organizations to decrease the cost for data processing and analytics. As health care generating volumes and variety of data it is required to build parallel algorithms that can support petabytes of data using hadoop and MapReduce parallel processing. K-means clustering is one of the methods for parallel algorithm. In order to build an accurate system large data sets need to be considered. Memory requirement increases with large data sets and algorithms become slow. Mahout scalable algorithms developed works better with huge data sets and improve the performance of the system. Mahout is an open source and can be used to solve problems arising with huge data sets. This paper proposes cloud based K-means clustering running as a MapReduce job. We use health care data on cloud for clustering. We then compare the results with various measures to conclude the best measure to find number of vectors in a given cluster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
T Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu, “An efficient K-means clustering algorithm: Analysis and implementation”, Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol 24, No 7, pp. 881–892, 2002.
White, T: Hadoop the definitive guide, O’Reilly Media, 2009.
Fredrik Farnstorm, J: Scalability for clustering algorithms revisited—SIGKDD Explorations, 2002, 2, pp. 51–57.
Rui Maximo Esteves, Chunming Rong, Rui Pais: K-means clustering in the cloud—a Mahout test, IEEE 2011 Workshops of international conference on Advanced information networking and application, pp. 514–519.
http://hadoop.apache.org/docs/r2.7.0/hadoop-project-dist/hadoopcommon/NativeLibraries.html.
Jain, A.K. and R.C Dubes, 1998: Algorithms for Clustering Data, Prentince Hall, New Jersy.
Dweepna Garg, Kushboo Trivedi, Fuzzy k-mean clustering in MapReduce on cloud based Hadoop, 2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).
Lin Gu, Zhonghua sheng, Zhiqiang Ma, Xiang Gao, Charles Zhang, Yaohui Jin: K Means of cloud computing: MapReduce, DVM, and windows Azure, Fourth International Conference on Cloud Computing, GRIDs, and Virtualization (cloud computing 2013). May 27–June 1, 2013, Valencia, Spain.
Budhaditya Saha, Dinh Phung, Duc-son Pham, Svetha Venkatesh, Clustering Patient Medical Records via sparse subspace representation from http://link.springer.com/chapter/10.1007/978-3-642-37456-2_11.
Sean Owen, Robin Anil, Ted Dunning, Ellen Friedman, Mahout in Action by Manning Shelter Island.
J. Dean and S. Ghemawat, “MapReduce simplified data processing on large clusters”, In Proc. Of the 6th Symposium on OS design and implementation (OSDI’04), Berkely, CA, USA, 2004, pp. 137–149.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Rallapalli, S., Gondkar, R.R., Madhava Rao, G.V. (2016). Cloud Based K-Means Clustering Running as a MapReduce Job for Big Data Healthcare Analytics Using Apache Mahout. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_14
Download citation
DOI: https://doi.org/10.1007/978-81-322-2755-7_14
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2753-3
Online ISBN: 978-81-322-2755-7
eBook Packages: EngineeringEngineering (R0)