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Survey of clustering algorithms.

Rui Xu1, Donald Wunsch

  • 1Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla, MO 65409, USA. rxu@umr.edu

IEEE Transactions on Neural Networks
|June 9, 2005
PubMed
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This study surveys diverse cluster analysis algorithms for data mining and machine learning. It clarifies algorithm choices and applications in fields like bioinformatics.

Area of Science:

  • Data Science
  • Computer Science
  • Machine Learning

Background:

  • Data analysis is crucial for understanding phenomena.
  • Cluster analysis is a fundamental data exploration technique used across various scientific domains.
  • The wide array of clustering algorithms can lead to confusion for researchers.

Purpose of the Study:

  • To provide a comprehensive survey of clustering algorithms.
  • To clarify the diversity of clustering tools available.
  • To illustrate the application of clustering in diverse fields.

Main Methods:

  • Surveying clustering algorithms from statistics, computer science, and machine learning.
  • Illustrating applications using benchmark datasets, the traveling salesman problem, and bioinformatics.

Related Experiment Videos

  • Discussing related topics such as proximity measures and cluster validation.

    • Main Results:

    • A structured overview of various clustering algorithms is presented.
    • Applications demonstrate the utility of clustering in benchmark datasets, the traveling salesman problem, and bioinformatics.
    • Key related concepts like proximity measures and cluster validation are examined.

    Conclusions:

    • The survey aims to reduce confusion by organizing the profusion of clustering options.
    • Understanding diverse clustering algorithms and their applications is essential for effective data analysis.
    • This work provides a valuable resource for researchers applying clustering techniques in statistics, computer science, and machine learning.