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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... define and redefine the problem , to determine relevant aspects of the problem , to supply the data , to remove errors from the data , to provide constraints on possible patterns , to interpret patterns and possibly reject those that ...
... definition to presentation and delivery of the resulting patterns was developed in the CRISP- DM project ( Chapman , et al . , 2000 ) and is becoming a de facto industrial standard . The MiningMart project ( Morik and Scholz , 2003 ) ...
... defined measures of interestingness and validity , respectively ) . Related research areas include database technology and data warehouses , statistics , machine learning , pattern recognition and soft computing , text and web mining ...
... defined in advance . However , the typical operations on data warehouses were similar to the ones from the traditional OLTP databases in that the user issued a query and received a data table as a result . The major 4 Chapter 1.
... defined scenarios , a typical operation in OLAP affects up to millions of records ( sometimes all records ) in the database in a non - predefined way . What is the role of data mining in the above framework ? While typical questions in ...
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 |