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Clustering I: Basic Clustering Models and Algorithms

In: Neural Networks and Statistical Learning

Author

Listed:
  • Ke-Lin Du

    (Concordia University, Department of Electrical and Computer Engineering
    Xonlink Inc.)

  • M. N. S. Swamy

    (Concordia University, Department of Electrical and Computer Engineering)

Abstract

Clustering is an unsupervised classification technique that identifies some inherent structure present in a set of objects based on a similarity measure. Clustering methods can be derived from statistical models or competitive learning and correspondingly they can be classified into generative (or model-based) and discriminative (or similarity-based) approaches. A clustering problem can also be modeled as a COP. Clustering neural networks are statistical models, where a probability density function (pdf) for data is estimated by learning its parameters. In this chapter, our emphasis is placed on a number of competitive learning-based neural networks and clustering algorithms. We describe the SOM, learning vector quantizationVector quantization (LVQ), and ART models, as well as C-means, subtractive, and fuzzy clustering algorithms.

Suggested Citation

  • Ke-Lin Du & M. N. S. Swamy, 2019. "Clustering I: Basic Clustering Models and Algorithms," Springer Books, in: Neural Networks and Statistical Learning, edition 2, chapter 0, pages 231-274, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4471-7452-3_9
    DOI: 10.1007/978-1-4471-7452-3_9
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