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A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning.

Ying Yang1, Haoyu Chen2, Haoshen Wu3

  • 1College of Information and Intelligence, Hunan Agricultural University, Changsha, China.

Peerj. Computer Science
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fuzzy clustering framework to handle missing data effectively. Improved algorithms demonstrate superior clustering accuracy for incomplete datasets compared to traditional methods.

Keywords:
Feature weightsFuzzy C-MeansIncomplete dataKernel functionNPSOCS

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Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Missing data poses a significant challenge for traditional clustering algorithms.
  • Existing methods often pre-process incomplete data, potentially impacting accuracy.
  • There is a need for integrated approaches that handle missing data during clustering.

Purpose of the Study:

  • To propose a generalized fuzzy clustering framework that combines data completion and clustering.
  • To enhance clustering accuracy for datasets with missing values.
  • To develop improved algorithms for clustering incomplete data.

Main Methods:

  • Developed a generalized fuzzy clustering framework using optimal completion strategy (OCS) and nearest prototype strategy (NPS).
  • Introduced feature weights to mitigate outlier influence on cluster centers.
  • Incorporated kernel functions to address linear indistinguishability problems.
  • Proposed four improved algorithms, including NPS-WKFCM and OCS-WKFCM.

Main Results:

  • The proposed algorithms, particularly NPS-WKFCM and OCS-WKFCM, showed superior clustering accuracy across datasets with varying missing rates.
  • Evaluated performance using correct clustering rate, iteration number, and external evaluation indexes.
  • Compared against seven conventional algorithms, demonstrating enhanced performance.

Conclusions:

  • The integrated approach of combining data completion and clustering significantly improves accuracy for incomplete datasets.
  • The feature weighted kernel fuzzy C-means algorithms with OCS and NPS offer a robust solution for clustering challenges posed by missing data.
  • The enhanced algorithms provide a superior method for clustering incomplete data compared to existing techniques.