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

Authors(1) :-Avula Chitty

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.

Authors and Affiliations

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

Data Mining, Data Stream Clustering, Density-Based Clustering

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-05-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 943-950
Manuscript Number : CSEIT1831120
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Avula Chitty, "Efficiency of Clustering Data Streams Based on Micro-Clusters Shared Density ", International 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.
Journal URL : http://ijsrcseit.com/CSEIT1831120

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