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Summary
This summary is machine-generated.

A new dynamic clustering algorithm for short text streams (DCSS) automatically determines topic numbers and handles topic drift. This method improves short text analysis by considering past data distributions for current stream clustering.

Keywords:
Dirichlet processDynamic clusteringShort text streamTopic drift

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Short text data from social media is rapidly increasing, posing challenges for traditional stream clustering.
  • Existing methods struggle with inferring the number of topics and addressing topic drift in short text streams.

Purpose of the Study:

  • To propose a dynamic clustering algorithm for short text streams (DCSS) that addresses topic number inference and topic drift.
  • To overcome the sparsity and weak signal issues inherent in short text data.

Main Methods:

  • Developed a dynamic clustering algorithm for short text streams (DCSS) based on the Dirichlet process.
  • DCSS leverages the correlation of topic distributions at neighboring time points.
  • It uses past document topic distributions as a prior for current distributions, allowing new data to update the posterior.

Main Results:

  • DCSS automatically learns the number of topics within documents.
  • The algorithm effectively solves the topic drift problem in short text streams.
  • Experimental results on two datasets demonstrate DCSS outperforms existing methods in performance and stability.

Conclusions:

  • DCSS offers an effective solution for short text stream clustering, addressing key limitations of prior approaches.
  • The method's ability to handle topic drift and infer topic numbers makes it suitable for dynamic, high-velocity data.
  • DCSS shows improved stability and performance, making it a valuable tool for analyzing social media and other short text data streams.