Data Mining and Decision Support: Integration and Collaboration
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.
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.
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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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TEXT AND WEB MINING
INTEGRATION OF DATA MINING AND DECISION
COLLABORATION IN A DATA MINING VIRTUAL
DATA MINING PROCESSES AND COLLABORATION
MINING 21 YEARS OF
ANALYSIS OF A DATABASE OF RESEARCH PROJECTS
WEBSITE ACCESS ANALYSIS FOR A NATIONAL
FIVE DECISION SUPPORT APPLICATIONS
COLLABORATIVE DATA MINING WITH RAMSYS
LESSONS LEARNED FROM DATA MINING DECISION
ACADEMIABUSINESS PARTNERSHIP MODELS
PREPROCESSING FOR DATA MINING AND DECISION
DATA MINING AND DECISION SUPPORT INTEGRATION
APPLICATIONS OF DATA MINING
Subject index 271