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Related Experiment Videos

Efficient streaming text clustering.

Shi Zhong1

  • 1Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. zhong@cse.fau.edu

Neural Networks : the Official Journal of the International Neural Network Society
|August 9, 2005
PubMed
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Clustering high-dimensional text streams requires forgetting past data for adaptability. An online spherical k-means algorithm with a forgetting factor achieves efficient and adaptive text stream clustering.

Area of Science:

  • Data Mining
  • Machine Learning
  • Natural Language Processing

Background:

  • Clustering streaming data is a growing research area with many real-world applications.
  • Clustering high-dimensional streaming text data remains a significant challenge due to data volume and velocity.
  • Existing scalable clustering strategies struggle with memory limitations and multiple data scans.

Purpose of the Study:

  • To develop a fast and adaptive algorithm for clustering high-dimensional streaming text data.
  • To combine an efficient online spherical k-means (OSKM) algorithm with a scalable clustering strategy.
  • To enhance the algorithm's adaptability to evolving data streams through a forgetting mechanism.

Main Methods:

  • Modified the spherical k-means (SPKM) algorithm into an online spherical k-means (OSKM) algorithm using Winner-Take-All competitive learning for online centroid updates.

Related Experiment Videos

  • Integrated OSKM with a scalable clustering strategy designed for large datasets that exceed memory capacity.
  • Introduced a forgetting factor applying exponential decay to historical data's importance, making the algorithm adaptive.

    • Main Results:

    • The proposed OSKM algorithm demonstrated efficiency comparable to SPKM but with superior clustering quality.
    • The combined approach effectively handles large-scale text streams within limited memory constraints.
    • Experimental results confirmed the algorithm's efficiency and adaptability in clustering text streams.

    Conclusions:

    • The developed algorithm provides an efficient and adaptive solution for clustering high-dimensional text streams.
    • A key finding is that forgetting historical data is crucial for achieving adaptability in text stream clustering.
    • The research highlights the importance of dynamic weighting of data based on recency for effective stream analysis.