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Adaptive Initialization Method for K-Means Algorithm.

Jie Yang1, Yu-Kai Wang1, Xin Yao2,3

  • 1Computational Intelligence and Brain Computer Interface Lab, Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney, Sydney, NSW, Australia.

Frontiers in Artificial Intelligence
|December 13, 2021
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Summary
This summary is machine-generated.

This study introduces an adaptive initialization method for K-means clustering (AIMK) to improve performance and stability. AIMK-RS offers reduced complexity for large datasets, outperforming existing methods.

Keywords:
adaptiveclusteringinitial cluster centersinitialization methodk-means

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Traditional K-means clustering suffers from suboptimal results due to random initialization.
  • This limitation can lead to poor clustering performance and instability, especially with complex datasets.

Purpose of the Study:

  • To propose an adaptive initialization method for K-means (AIMK) to enhance clustering performance and stability.
  • To develop a scalable version (AIMK-RS) for large and high-dimensional datasets.
  • To evaluate AIMK and AIMK-RS against existing initialization techniques and clustering algorithms.

Main Methods:

  • Developed an adaptive initialization method for K-means (AIMK).
  • Incorporated random sampling into AIMK (AIMK-RS) for improved efficiency on large datasets.
  • Conducted comparative experiments on 22 real-world datasets.

Main Results:

  • AIMK and AIMK-RS demonstrated superior performance compared to current initialization methods.
  • AIMK-RS achieved significant time complexity reduction to O(n).
  • AIMK successfully improved K-medoids and spectral clustering, showing broad applicability.

Conclusions:

  • AIMK and AIMK-RS offer superior performance, stability, and scalability for K-means clustering.
  • The proposed methods show potential for application in various partition-based clustering algorithms.
  • AIMK-RS provides an efficient solution for large-scale clustering tasks.