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A Modified MinMax k-Means Algorithm Based on PSO.

Xiaoyan Wang1, Yanping Bai2

  • 1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.

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|September 23, 2016
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Summary
This summary is machine-generated.

This study introduces a modified MinMax k-means algorithm using Particle Swarm Optimization (PSO) to automatically find optimal parameters. This approach achieves lower clustering errors compared to existing methods.

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Area of Science:

  • Data Mining
  • Machine Learning
  • Clustering Algorithms

Background:

  • The MinMax k-means algorithm minimizes maximum intraclustering errors to mitigate poor initialization.
  • It involves an exponent and a memory parameter, requiring careful selection for optimal performance.
  • Existing frameworks adapt the exponent parameter but do not guarantee the lowest clustering errors.

Purpose of the Study:

  • To modify the MinMax k-means algorithm using Particle Swarm Optimization (PSO).
  • To automatically determine optimal parameter values for achieving the lowest clustering errors.
  • To improve upon the original MinMax k-means and standard k-means algorithms.

Main Methods:

  • Modified the MinMax k-means algorithm by integrating Particle Swarm Optimization (PSO).
  • PSO was employed to search for the optimal exponent and memory parameters.
  • The proposed method was tested on benchmark datasets under various initial conditions.

Main Results:

  • The modified MinMax k-means algorithm with PSO demonstrated the ability to automatically find parameters yielding the lowest clustering errors.
  • Experimental results showed superior performance compared to the standard k-means and original MinMax k-means algorithms.
  • The algorithm consistently achieved lower intraclustering errors across different datasets and initializations.

Conclusions:

  • The proposed PSO-based parameter optimization effectively enhances the MinMax k-means algorithm.
  • This approach overcomes limitations of previous parameter adaptation methods.
  • The modified algorithm offers a robust solution for achieving minimal clustering errors automatically.