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An Effective Feature Selection Technique for Mining High Dimensional Data on Bigdata

K. Bhaskar Naik*, S.P Sindhuja**

* Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

** PG Scholar, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

Periodicity:November - January'2016

Abstract

In the recent years, many research innovations have come into foray in the area of big data analytics. Advanced analysis of the big data stream is bound to become a key area of data mining research as the number of applications requiring such processing increases. Big data sets are now collected in many fields eg., Finance, business, medical systems, internet and other scientific research. Data sets rapidly increase their size as they are often generated in the form of incoming stream. Feature selection has been used to lighten the processing load in inducing a data mining model, but mining a high dimensional data becomes a tough task due to its exponential growth of size. This paper aims to compare the two algorithms, namely Particle Swarm Optimization and FAST algorithm in the feature selection process. The proposed algorithm FAST is used in order to reduce the irrelevant and redundant data, while streaming high dimensional data which would further increase the analytical accuracy for a reasonable processing time.

Keywords

Feature Selection, Minimum Spanning Tree, Classification

How to Cite this Article?

Naik, K. B., and Sindhuja, S. P. (2016). An Effective Feature Selection Technique for Mining High Dimensional Data on Bigdata. i-manager’s Journal on Cloud Computing, 3(1), 18-23.

References

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