Data Mining and Decision Support: Integration and Collaboration

Front Cover
Dunja Mladenic, Nada Lavrač, Marko Bohanec, Steve Moyle
Springer Science & Business Media, Dec 6, 2012 - Computers - 275 pages

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.
Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses.

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 74

Page xiii
... 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 are ...
Page xiv
... definition to presentation and delivery of the resulting patterns was developed in the CRISPDM project (Chapman, et al., 2000) and is becoming a de facto industrial standard. The MiningMart project (Morik and Scholz, 2003) developed ...
Page 3
... 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, and ...
Page 4
... 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 difference 4 Chapter 1.
Page 5
... 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 OLTP ...

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Selected pages

Contents

DATA MINING
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
Copyright

Other editions - View all

Data Mining and Decision Support: Integration and Collaboration
Dunja Mladenic,Nada Lavrač,Marko Bohanec,Steve Moyle
Limited preview - 2003
Data Mining and Decision Support: Integration and Collaboration
Dunja Mladenic,Nada Lavrač,Marko Bohanec,Steve Moyle
No preview available - 2012

Common terms and phrases

accident applications approach Artificial Intelligence association rules attributes Bohanec brand Chapter classifiers client clustering collaborative data mining convex hull CRISP-DM customers data analysis data mining methods data mining models data mining process data mining project Data Mining Virtual data preparation data transformation data understanding database dataset decision analysis decision models decision support systems decision support techniques decision tree decision-making defined described DEXi DMVO documents domain experts evaluation example Figure goal Grobelnik hierarchical induction integration Jožef Stefan Institute Knowledge Discovery knowledge management Lavrač Ljubljana machine learning marketing methodology mining and decision Mining Virtual Organization Mladenić Moyle multi-attribute model node options phase PMML prediction problem solving Proc relational data mining remote collaboration ROC analysis Section selection Slovenia Soleunet Project solutions specific subgroup discovery SumatraTT target task techniques text mining typically virtual enterprise visualization Web mining

Bibliographic information

QR code for Data Mining and Decision Support
TitleData Mining and Decision Support: Integration and Collaboration
Volume 745 of The Springer International Series in Engineering and Computer Science
EditorsDunja Mladenic, Nada Lavrač, Marko Bohanec, Steve Moyle
Editionillustrated, reprint
PublisherSpringer Science & Business Media, 2012
ISBN1461502861, 9781461502869
Length275 pages
Subjects ›  › 

Computers / Artificial Intelligence / Computer Vision & Pattern Recognition
Computers / Artificial Intelligence / General
Computers / Data Science / Data Analytics
Computers / Information Technology
Computers / Information Theory
Computers / Programming / Algorithms
  
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