• Sanghamitra Bandyopadhyay
  • Sriparna Saha


This chapter provides an introduction to the clustering problem, the different data types used, problems of model and model order selection, outliers, and the research issues, challenges and application domains. The chapter starts with a brief overview of the different data types e.g., binary, categorical, ordinal and quantitative, with several examples. Thereafter the steps in automatic machine recognition of patterns are described in detail, including the procedures of data collection, feature selection, classification and clustering. Different distance measures used for clustering are then mentioned in brief. Some ways to deal with outliers and missing values present in a data set are described. Finally, applications of pattern recognition techniques in different domains are highlighted.


Feature Selection Cluster Algorithm Multiobjective Optimization Cluster Technique Linear Discriminant Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sanghamitra Bandyopadhyay
    • 1
  • Sriparna Saha
    • 2
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia
  2. 2.Dept. of Computer ScienceIndian Institute of TechnologyPatnaIndia

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