Enhanced Map Reduce Techniques for Big Data Analytics based on K-Means Clustering
S. Dhanasekaran, Raj Sundarrajan, B. S. Murugan, S. Kalaivani, Venkatesh Mathan Kumar Vasudevan · 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) · 2019
The Clustering methods have been greatly adopted in various real world data analysis applications, such as customer behavior analysis, medical data analysis, digital forensics, etc. In existing system, MR-mafia subspace clustering algorithm becomes inefficient as well as ineffective because the data size are continuously increasing, and data blocks are overlaying. Big Data environment inherits several knowledge and we extracts the necessary knowledge and K-means clustering algorithm is being designed. This paper focused on K - mean clustering algorithm based on improved map reduce techniques. The algorithm takes advantages to avoid unnecessary input and output data and also used to optimize data storage and also to achieve the out sourcing of data privacy. We have using a medical datasets of this project, and Enhanced map reduce based K - means clustering algorithm have been proposed which work effectively done and that can be outsourced to cloud server.