Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering

Guanli Yue, Yanpeng Qu, Longzhi Yang, Changjing Shang, Ansheng Deng, Fei Chao, Qiang Shen

Research output: Contribution to journalArticlepeer-review

80 Downloads (Pure)

Abstract

Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates the comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters, which are challenging to be determined for different data types. This article proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modeling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

Original languageEnglish
Pages (from-to)3305-3318
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Volume31
Issue number10
Early online date22 Feb 2023
DOIs
Publication statusPublished - 01 Oct 2023

Keywords

  • Clustering
  • Clustering algorithms
  • Fuzzy sets
  • Fuzzy-rough set
  • Harmonic analysis
  • Harmonic discrepancy
  • Noise measurement
  • Phase change materials
  • Rough set
  • Rough sets
  • Uncertainty
  • harmonic discrepancy
  • rough set
  • fuzzy-rough set

Fingerprint

Dive into the research topics of 'Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering'. Together they form a unique fingerprint.

Cite this