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A Belief Two-Level Weighted Clustering Method for Incomplete Pattern Based on Multiview Fusion.

Zong-Fang Ma1, Hui-Xuan Zhao1, Lei-Hua Li1

  • 1College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710311, China.

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This study introduces a novel belief two-level weighted clustering (BTC-MV) method for incomplete data. BTC-MV effectively handles missing attributes and integrates multiple data views, significantly reducing clustering error rates.

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Area of Science:

  • Data Mining
  • Machine Learning
  • Pattern Recognition

Background:

  • Incomplete pattern clustering is hindered by missing data uncertainty.
  • Single-view clustering methods overlook valuable multi-view information.

Purpose of the Study:

  • To propose a novel belief two-level weighted clustering method based on multi-view fusion (BTC-MV).
  • To address challenges in incomplete pattern clustering by effectively handling missing data and leveraging multi-view information.

Main Methods:

  • Attribute-level weighted imputation using k-nearest neighbor (KNN) strategy for missing data estimation.
  • Multi-view clustering on imputed data with view weights reflecting evidence reliability.
  • View-level weighted fusion using belief function theory to integrate multi-view membership values.

Main Results:

  • The proposed BTC-MV method demonstrated a lower error rate compared to classical methods (MI-KM, MI-KMVC, KNNI-FCM, KNNI-MFCM).
  • Experiments on six UCI datasets confirmed the superior performance of BTC-MV in incomplete pattern clustering.

Conclusions:

  • The BTC-MV method offers a robust solution for incomplete pattern clustering.
  • Multi-view fusion and belief function theory significantly enhance clustering accuracy with incomplete data.