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CLUSTERING OF DATA WITH MISSING ENTRIES.

Sunrita Poddar1, Mathews Jacob1

  • 1Department of Electrical and Computer Engineering, University of Iowa, IA, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|February 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new clustering algorithm designed to handle missing data effectively. The method uses an optimization approach to recover clusters, showing strong performance on various datasets with significant missing entries.

Keywords:
clusteringmissing entriesnon-convex penalties

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Area of Science:

  • Machine Learning
  • Data Science
  • Optimization

Background:

  • Analysis of large datasets is hindered by missing entries, as most machine learning algorithms require complete data.
  • Existing methods struggle with datasets containing significant amounts of missing information.

Purpose of the Study:

  • To develop a novel clustering algorithm capable of performing well with incomplete datasets.
  • To address the challenge of data imputation within clustering frameworks.

Main Methods:

  • Introduced an ℓ0 fusion penalty-based optimization problem for cluster recovery.
  • Developed an algorithm to solve a relaxed version using saturating non-convex fusion penalties.
  • Theoretically analyzed conditions for successful cluster recovery.

Main Results:

  • The proposed algorithm demonstrates effective clustering even with large fractions of missing data.
  • Performance was validated on both simulated and real-world datasets.
  • The technique successfully recovers clusters in the presence of missing entries.

Conclusions:

  • The developed clustering algorithm offers a robust solution for analyzing datasets with missing entries.
  • This work contributes a new approach to handling incomplete data in machine learning.
  • The method shows promise for practical applications involving real-world datasets.