Hybrid Clustering Algorithm ‘KCu’ for Combining the Features of K-Means and CURE Algorithm for Efficient Outliers Handling

Hybrid Clustering Algorithm ‘KCu’ for Combining the Features of K-Means and CURE Algorithm for Efficient Outliers Handling

B. Renuka DeviS. Pallam Setty

Department of CSE, Vignan’s Nirula Institute of Technology & Science for Women, Guntur 522005, Andhra Pradesh, India

Department of CS & SE, College of Engineering, Andhra University, Andhra Pradesh 530003, India

Corresponding Author Email: 
dr.b.renukacse@gmail.com
Page: 
76-79
|
DOI: 
https://doi.org/10.18280/ama_b.610204
Received: 
26 April 2018
|
Accepted: 
2 June 2018
|
Published: 
30 June 2018
| Citation

OPEN ACCESS

Abstract: 

In the ongoing situation, the volume of information expands step by step. By the year 2020 the volume of Big Data would reach up to 40zb according to International Data Corporation (IDC). Big Data has turned out to be prevalent for handling, putting away and overseeing huge volumes of information. The grouping of datasets has turned into a testing issue in the field of Big Data examination; however, there are entanglements for applying conventional bunching calculations to huge information because of expanding the volume of information step by step. In this manuscript a new hybrid clustering algorithm, namely KCu to combine the features of both K-Means and CURE clustering algorithms is proposed. The proposed algorithm first applies k-means on data set and then applies CURE on resultant clusters from k-means. We experimented KCu and we show that, when compared to k-means and Cure. Which gives accurate results because of CURE? CURE can handle outliers and it gives non spherical shapes it is the disadvantage of other clustering algorithm.

Keywords: 

big data, clustering, partitioning, hierarchical k-means, CURE hybrid algorithm

1. Introduction
2. Related Work
3. Clustering Techniques
4. Hybrid Clustering Method
5. Results
6. Conclusion
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