Clustering I: Basic Clustering Models and Algorithms

Ke-Lin Du () and M. N. S. Swamy
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Ke-Lin Du: Concordia University, Department of Electrical and Computer Engineering
M. N. S. Swamy: Concordia University, Department of Electrical and Computer Engineering

Chapter Chapter 9 in Neural Networks and Statistical Learning, 2019, pp 231-274 from Springer

Abstract: 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.

Date: 2019
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DOI: 10.1007/978-1-4471-7452-3_9

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