## 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

**Section**2 provides the background to data mining,

**Section**3 describes — in a simplified manner – some data mining techniques that proved to be useful in the development of applications described in this book.

To conclude this

**section**, we cite (Friedman, 1997) stating why data mining holds the intellectual, academic and commercial future: “Every time the amount of data increases by a factor of ten, we should totally rethink how we analyze it.

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.

In the following

**sections**we outline some data mining tasks, discuss the appropriate methods for solving the tasks and illustrate the results of applying these methods through examples. 3.1 ...

Among these, the K-Means method is one of the most popular. This method was applied in an application described in Chapter 11. Figure 1-2. A dendrogram, constructed through hierarchical clustering. This

**section**Data mining 11.

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### Contents

1 | |

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 |

WEBSITE 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 |