A Modified Approach of OPTICS Algorithm for Data Streams

  • M. Shukla Department of Computer Engineering, Marwadi Education Foundation, India | R. K. University, Rajkot, India
  • Y. P. Kosta Department of Computer Engineering, Marwadi Education Foundation, Rajkot, India
  • M. Jayswal Department of Computer Engineering, Marwadi Education Foundation, Rajkot, India
Volume: 7 | Issue: 2 | Pages: 1478-1481 | April 2017 | https://doi.org/10.48084/etasr.963

Abstract

Data are continuously evolving from a huge variety of applications in huge volume and size. They are fast changing, temporally ordered and thus data mining has become a field of major interest. A mining technique such as clustering is implemented in order to process data streams and generate a set of similar objects as an individual group. Outliers generated in this process are the noisy data points that shows abnormal behavior compared to the normal data points. In order to obtain the clusters of pure quality outliers should be efficiently discovered and discarded. In this paper, a concept of pruning is applied on the stream optics algorithm along with the identification of real outliers, which reduces memory consumption and increases the speed for identifying potential clusters.

Keywords: two phase, cluster quality, clustering technique, pruning, time and space complexity, threshold value

Author Biography

M. Shukla, Department of Computer Engineering, Marwadi Education Foundation, India | R. K. University, Rajkot, India

Marwadi Education Foundation, Rajkot, Gujarat, India

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