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A new Growing Neural Gas for clustering data streams.

Mohammed Ghesmoune1, Mustapha Lebbah1, Hanene Azzag1

  • 1University of Paris 13, Sorbonne Paris City LIPN-UMR 7030 - CNRS, 99, av. J-B Clément-F-93430 Villetaneuse, France.

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Clustering massive datasets efficiently requires processing data streams. G-Stream, a novel algorithm, clusters data streams in one pass using growing neural gas, improving efficiency and cluster quality.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Clustering massive datasets presents challenges in memory and time constraints.
  • Efficiently partitioning continuous data streams is crucial for big data analysis.

Purpose of the Study:

  • To introduce G-Stream, a novel algorithm for clustering data streams.
  • To enable efficient, single-pass clustering of massive datasets without prior cluster number assumptions.

Main Methods:

  • The G-Stream algorithm utilizes a growing neural gas approach.
  • It incorporates a reservoir and a fading function to enhance clustering quality.
  • The algorithm processes data streams in a single pass.

Main Results:

  • G-Stream effectively clusters data streams with arbitrary shapes.
  • The use of a reservoir and fading function improves clustering performance.
  • The algorithm demonstrates efficiency on public datasets.

Conclusions:

  • G-Stream offers an efficient solution for clustering data streams.
  • The algorithm's adaptability to arbitrary cluster shapes and its performance make it suitable for massive datasets.