## Data Mining and Decision Support: Integration and CollaborationDunja Mladenic, Nada Lavrač, Marko Bohanec, Steve Moyle Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting. |

### From inside the book

Results 1-5 of 86

... belong . Supervised learning assumes that training examples are classified whereas unsupervised learning concerns the analysis of unclassified examples . In the following sections we outline some data mining

**tasks**Data mining 7.

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**tasks**, discuss the appropriate methods for solving the

**tasks**and illustrate the results of applying these ...

**task**at hand is to find a classifier that will enable a newly encountered instance to be classified . Examples of ...

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**task**of this type was defined by the following question : " Which other journals / magazines do readers of a particular journal / magazine read ? " As opposed to decision tree and rule set induction , such a question is best addressed ...

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**task**, as in the case of propositional algorithms SD ( Gamberger and Lavrač , 2002 ) and CN2 - SD ( Lavrač , et al . , 2002a ) , and a relational subgroup discovery algorithm RSD ( Lavrač , et al . , 2002b ) . A sample subgroup ...

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**task**( addressed in Chapter 11 ) was to use a subset of this database - 8000 questionnaires available for 1998 - to answer some questions like " What are the attributes of individuals that are consumers of a particular media offer ...

### Contents

3 | |

TEXT AND WEB MINING | 15 |

DECISION SUPPORT | 23 |

INTEGRATION OF DATA MINING AND DECISION | 37 |

COLLABORATION IN A DATA MINING VIRTUAL | 49 |

DATA MINING PROCESSES AND COLLABORATION | 63 |

AN INTRODUCTION | 80 |

SUPPORTING | 91 |

MINING 21 YEARS OF | 142 |

ANALYSIS OF A DATABASE OF RESEARCH PROJECTS | 157 |

WEB SITE ACCESS ANALYSIS FOR A NATIONAL | 167 |

FIVE DECISION SUPPORT APPLICATIONS | 177 |

COLLABORATIVE DATA MINING WITH RAMSYS | 215 |

LESSONS LEARNED FROM DATA MINING DECISION | 237 |

A KNOWLEDGE | 247 |

ACADEMIABUSINESS PARTNERSHIP MODELS | 261 |

PREPROCESSING FOR DATA MINING AND DECISION | 107 |

DATA MINING AND DECISION SUPPORT INTEGRATION | 118 |

APPLICATIONS OF DATA MINING | 131 |

Subject index 271 | 270 |

### Other editions - View all

Data Mining and Decision Support: Integration and Collaboration Dunja Mladenic,Nada Lavrač,Marko Bohanec,Steve Moyle No preview available - 2012 |