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A Comprehensive Overview of Clustering Algorithms in Pattern Recognition PDF

8 Pages·2012·English
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About A Comprehensive Overview of Clustering Algorithms in Pattern Recognition

This document provides an overview of clustering algorithms used in pattern recognition. It discusses unsupervised learning techniques including K-means clustering and hierarchical clustering (agglomerative and divisive). For K-means clustering, it describes the steps, provides an example, and discusses advantages and disadvantages. For hierarchical clustering, it explains agglomerative clustering with steps and an example, and applications of these techniques. The document aims to compare K-means and hierarchical clustering and suggest the best approach for a given application.

Detailed Information

Author:['International Organization of Scientific Research (IOSR)']
Publication Year:2012
Pages:8
Language:English
Format:PDF
Price:FREE
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