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
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... data mining methods are able to deal with very large datasets in a very efficient way , while the algorithmic complexity of statistical methods may turn out to be prohibitive for their use on very large databases . Next , the results of ...
... data mining was sufficiently mature to be adopted as a key element of their business . This led to the development of the CRISP - DM Cross - Industry Standard Process for Data ... methods , including support for data mining functionality ( ...
... data mining is to serve as underlying technology for building ' big brother ' systems . 3 . SELECTED DATA MINING METHODS The most popular predictive data mining techniques are rule and decision tree learning techniques , which are ...
... data mining tasks , discuss the appropriate methods for solving the tasks and illustrate the results of applying these methods through examples . 3.1 Induction of models for classification and prediction In a classification task , data ...
... methods , studied and widely used in statistical data analysis ( Hartigan , 1975 , Sokal and Sneath , 1963 ) is hierarchical clustering . The hierarchical clustering algorithm starts by assigning each object to its own cluster , and ...
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 |