## Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of Technology, Chicago, 15–18 July 2004David Banks, Leanna House, Frederick R. McMorris, Phipps Arabie, Wolfgang A. Gaul Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas. |

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### Contents

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24 | |

The Last Step of a New Divisive Monothetic Clustering | 43 |

A SelfOrganizing Map for Dissimilarity Data | 61 |

Controlling the Level of Separation of Components in Monte | 77 |

Fixing Parameters in the Constrained Hierarchical | 85 |

Spatial Pyramidal Clustering Based on a Tessellation | 105 |

Relative Projection Pursuit and its Application | 122 |

Phylogenetic Closure Operations and HomoplasyFree | 393 |

Consensus of Classification Systems with Adams Results | 417 |

Determining Horizontal Gene Transfers in Species | 439 |

Mathematical and Statistical Modeling of Acute Inflammation | 457 |

Classifying the State of Parkinsonism by Using Electronic | 477 |

Subject Filtering for Passive Biometric Monitoring | 485 |

Mining Massive Text Data and Developing Tracking Statistics | 494 |

Contributions of Textual Data Analysis to Text Retrieval | 511 |

Priors for Neural Networks | 141 |

Phoneme Discrimination with Functional MultiLayer | 157 |

On Classification and Regression Trees for Multiple Responses | 177 |

CherryPicking as a Robustness Tool | 197 |

Modified Biplots for Enhancing TwoClass Discriminant | 233 |

Classification of Geospatial Lattice Data and their Graphical | 250 |

A Dimension Reduction Technique for Local Linear | 269 |

Optimal Discretization of Quantitative Attributes | 287 |

Dependencies in Bivariate IntervalValued Symbolic Data | 319 |

A Hausdorff Distance Between HyperRectangles | 333 |

Dynamic Cluster Methods for Interval Data Based | 351 |

Probabilistic Allocation of Aggregated Statistical Units | 371 |

Automated Resolution of Noisy Bibliographic References | 521 |

Choosing the Right Bigrams for Information Retrieval | 531 |

A Mixture Clustering Model for Pseudo Feedback | 541 |

Analysis of CrossLanguage OpenEnded Questions Through | 553 |

Inferring Users Information Context from User Profiles | 562 |

Database Selection for Longer Queries | 575 |

An Overview of Collapsibility | 586 |

Generalized Factor Analyses for Contingency Tables | 597 |

A PLS Approach to Multiple Table Analysis | 607 |

Simultaneous Row and Column Partitioning in Several | 621 |

The Treatment of Missing Values and its Effect on Classifier | 638 |

### Other editions - View all

Classification, Clustering, and Data Mining Applications David Banks,Leanna House,Frederick R McMorris No preview available - 2004 |

### Common terms and phrases

applied approach associated average bigrams binary biplot classification closure systems clustering methods coefficient collapsibility column computed considered constraints contingency table convex correlation corresponding criterion Data Analysis data mining data set database defined denote described Diday dimension reduction dissimilarity distance distribution edges elements estimated example function gene given graph Hausdorff distance hierarchical horizontal gene transfer imputation interval iteration k-means kernel likelihood linear regression matrix maximum measure minimizes misclassification missing data missing values mixture model modal multivariate node number of clusters observations obtained optimal p-adic parameters partition performance phylogenetic principal component analysis prior problem profiles projection index projection pursuit proposed Proteobacteria prototype pyramid quartet trees query model Rand index random reference represent representation rule sample Section selected similar simulated space Statistical step subset symbolic data symbolic objects Table ultrametric variables variance vector weight Yadidean