Efficiency of Clustering Data Streams Based on Micro-Clusters Shared Density

Authors

  • Avula Chitty  Department of CSE, Assistant Professor, Sri Indu College of Engineering And Technology, Hyderabad, Telangana, India

Keywords:

Data Mining, Data Stream Clustering, Density-Based Clustering

Abstract

As more and a lot of applications produce streaming information, clustering knowledge streams has become a very important technique for data and information engineering. A typical approach is to summarize the information stream in time with an online method into an oversized number of therefore known as micro-clusters. Micro-clusters represent native density estimates by aggregating {the information} of the many data points in an outlined area. On demand, a (modified) typical bunch formula is used in a very second offline step to recluster the micro-clusters into larger final clusters. For reclustering, the centers of the micro-clusters are used as pseudo points with the density estimates used as their weights. However, data concerning density within the area between micro-clusters isn't preserved within the on-line process and reclustering relies on probably inaccurate assumptions concerning the distribution of knowledge inside and between micro-clusters (e.g., uniform or Gaussian).This paper describes DBSTREAM, the primary micro-cluster-based on-line bunch part that expressly captures the density between micro-clusters via a shared density graph. The density data during this graph is then exploited for reclustering supported actual density between adjacent micro-clusters. We have a tendency to discuss the house and time complexness of maintaining the shared density graph. Experiments on a good vary of artificial and real knowledge sets highlight that mistreatment shared density improves bunch quality over alternative popular knowledge stream bunch ways that need the creation of a bigger variety of smaller micro-clusters to realize comparable results.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-05-30

Issue

Volume 2, Issue 3, May-June-2017

Section

Research Articles

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How to Cite

[1]
Avula Chitty, " Efficiency of Clustering Data Streams Based on Micro-Clusters Shared Density , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.943-950, May-June-2017.