MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction

  • Authors

    • Shyam Sunder Reddy K VASAVI COLLEGE OF ENGINEERING
    • Shoba Bindu C JNTU College of Engineering, Ananthapuram
    2018-03-13
    https://doi.org/10.14419/ijet.v7i2.9051
  • Data Stream, Data Mining, Density-Based Clustering, Grid-Based Clustering, Micro-Clusters.

  • Abstract

    Real-time data stream clustering has been widely used in many fields, and it can extract useful information from massive sets of data. Most of the existing density-based algorithms cluster the data streams based on the density within the micro-clusters. These algorithms completely omit the data density in the area between the micro-clusters and recluster the micro-clusters based on erroneous assumptions about the distribution of the data within and between the micro-clusters that lead to poor clustering results. This paper describes a novel density-based clustering algorithm for evolving data streams called MCDAStream, which clusters the data stream based on micro-cluster density and attraction between the micro-clusters. The attraction of micro-clusters characterizes the positional information of the data points in each micro-cluster. We generate better clustering results by considering both micro-cluster density and attraction of micro-clusters. The quality of the proposed algorithm is evaluated on various synthetic and real-time datasets with distinct characteristics and quality metrics.

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  • How to Cite

    K, S. S. R., & C, S. B. (2018). MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction. International Journal of Engineering & Technology, 7(2), 270-275. https://doi.org/10.14419/ijet.v7i2.9051

    Received date: 2018-01-05

    Accepted date: 2018-03-07

    Published date: 2018-03-13