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An Improved K-Means Algorithm Based on Evidence Distance.

Ailin Zhu1, Zexi Hua1, Yu Shi2

  • 1School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved k-means clustering algorithm using evidence distance, enhancing attribute differentiation and avoiding local optima. The new method demonstrates superior clustering effects and convergence compared to traditional approaches.

Keywords:
cluster analysisevidence distanceevidence theoryk-means clustering

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • K-means clustering is sensitive to initial centers and distance metrics.
  • Euclidean distance in traditional k-means leads to poor attribute differentiation and local optima.
  • Existing methods struggle with data exhibiting complex attribute relationships.

Purpose of the Study:

  • To propose an improved k-means algorithm utilizing evidence distance.
  • To address the limitations of Euclidean distance in k-means clustering.
  • To enhance clustering accuracy and convergence speed.

Main Methods:

  • Modeled sample point attribute values as basic probability assignments (BPA).
  • Replaced Euclidean distance with evidence distance for sample point measurement.
  • Implemented and tested the improved k-means algorithm on UCI data.

Main Results:

  • The evidence distance-based k-means algorithm showed improved clustering effects.
  • The improved algorithm exhibited better convergence properties.
  • Experimental results outperformed traditional k-means, aggregation distance k-means, and Gaussian mixture models.

Conclusions:

  • Evidence distance is a more effective metric for k-means clustering than Euclidean distance.
  • The proposed algorithm offers a robust solution for complex clustering tasks.
  • This approach enhances the reliability and performance of k-means clustering.