**Pattern recognition**

*Topics*

1. | Introduction. Recognition of patterns. | |

2. | Measures of prediction. | |

3. | The H-method of estimation. | |

4. | Distance measures. | |

5. | Graphic analysis. | |

6. | Case study | |

7. | Hierarchical clustering. | |

8. | K-means clustering. | |

9. | Examples | |

10. | Dimension analysis. | |

11. | Error rate analysis. | |

12. | Distance measures. | |

13. | Importance of variables. | |

14. | Quadratic procedures | |

15. | Trends in data. | |

16. | Regression trees | |

17. | Confidence intervals | |

18. | Gravity centers | |

19. | Sensitivity analysis | |

20. | Case study | |

21. | Guidelines for presentation of results |

Pattern recognition is important in many situations, both in applied sciences and industry. The approach of the H-method can be extended to the area of pattern recognition. The basic idea of the H-method is to generate weights of the variables such that the resulting 'profile vector' (score vector) has predictive performance. The results at each step is thus a 'representative' that is able to describe the data to some degree. Each profile vector can be divided into parts and several ones can be generated to represent the parts of data. This flexibility of generating one or more profile vectors from subsets of data makes it possible to recognize different patterns in data. This approach can be combined with other strategies of analysis.

An example is regression trees.

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