DMM-Stream: A Density Mini-Micro Clustering Algorithm for Evolving Data Streams

  • Amineh Amini
  • Hadi Saboohi
  • Teh Ying Wah
  • Tutut Herawan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)


Clustering real-time stream data is an important and challenging problem. The existing algorithms have not considered the distribution of data inside micro cluster, specifically when data points are non uniformly distributed inside micro cluster. In this situation, a large radius of micro cluster has to be considered which leads to lower quality. In this paper, we present a density-based clustering algorithm, DMM-Stream, for evolving data streams. It is an online-offline algorithm which considers the distribution of data inside micro cluster. In DMM-Stream, we introduce mini-micro cluster for keeping summary information of data points inside micro cluster. In our method, based on the distribution of the dense areas inside the micro cluster at least one representative point, either micro cluster itself or its mini-micro clusters’ centers, are sent to the offline phase. By choosing a proper mini-micro and micro center, we increase cluster quality while maintaining the time complexity. A pruning strategy is also used to filter out the real data from noise by introducing dense and sparse mini-micro and micro cluster. Our performance study over real and synthetic data sets demonstrates effectiveness of our method.


Density-based clustering Micro cluster Mini-micro cluster 


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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Amineh Amini
    • 1
  • Hadi Saboohi
    • 1
  • Teh Ying Wah
    • 1
  • Tutut Herawan
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of Malaya (UM)Kuala LumpurMalaysia

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