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Clustering I: Basic Clustering Models and Algorithms

  • Ke-Lin DuEmail author
  • M. N. S. Swamy
Chapter

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 quantization (LVQ), and ART models, as well as C-means, subtractive, and fuzzy clustering algorithms.

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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada
  2. 2.Xonlink Inc.HangzhouChina

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