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A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

Entropy, ISSN: 1099-4300, Vol: 25, Issue: 3
2023
  • 3
    Citations
  • 0
    Usage
  • 11
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    3
  • Captures
    11
  • Mentions
    2
    • Blog Mentions
      1
      • 1
    • News Mentions
      1
      • 1

Most Recent Blog

Entropy, Vol. 25, Pages 510: A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

Entropy, Vol. 25, Pages 510: A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy Entropy doi: 10.3390/e25030510 Authors: Xinzhi Du Since the

Most Recent News

Study Findings on Algorithms Described by a Researcher at Anhui University of Technology (A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy)

2023 MAR 30 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- New study results on algorithms have been published. According

Article Description

Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enhances the efficiency of extracting the contribution of different features. The first entropy term helps to minimize the clusters’ dispersion and maximize the negative entropy to control the clustering process, which also promotes the association between the samples. The second entropy term helps to control the weights of features since different features have different weights in the clustering process. Experiments on real-world datasets indicate that the proposed algorithm gives better clustering results than other algorithms. The experiments demonstrate the proposed algorithm’s robustness by analyzing the parameters’ sensitivity and comparing the computational distance formulas. In summary, the improved algorithm improves classification performance under noisy interference and high-dimensional datasets, increases computational efficiency, performs well in real-world high-dimensional datasets, and encourages the development of robust noise-resistant high-dimensional fuzzy clustering algorithms.

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