## 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 59

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**goals**of integration , collaboration , education , and business issues . In our work , we were supported also by the Advisory Board , whose main members were Maarten van Someren , David Pearce , Jane McKenzie , Jörg - Uwe Kietz and ...

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**goals**of statistics is hypothesis testing , one of the main

**goals**of data mining is the generation of understandable hypotheses . To conclude this section , we cite ( Friedman , 1997 ) stating why data mining holds the intellectual ...

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**goals**and the actual

**goals**of data mining . 2. Data understanding : familiarization with the data and the application domain , by exploring and defining the relevant prior knowledge . 3. Data preparation through data cleaning and ...

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**goal**of data analysis may be different . Instead of model construction , the

**goals**may be the discovery of individual patterns / rules describing regularities in the data . In the Mediana dataset analysis ( see Chapter 11 ) , a task ...

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**goal**of clustering is to partition a set of data into groups , called clusters , such that the members of each group share some interesting common properties . Given data about a set of objects , a clustering algorithm creates groups of ...

### 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 |