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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SELECTED DATA MINING METHODS The most popular predictive data mining techniques are rule and decision tree learning techniques, which are mostly concerned with the discovery of classificatory properties of data tables.
Two methods that result in classification models are outlined: decision tree induction and rule set induction. 3.1.1 Decision tree induction A decision tree is one type of data mining model having a structure consisting of a number of ...
A decision tree, modeling the profile of readers of a teenage magazine. The induced set of rules can be used for classification. A rule set is usually interpreted in a top-down manner as if-then-else rules (a decision list) and the ...
While a decision tree and a set of rules represent a model that can be used for classification and/or prediction, the goal of data analysis may be different. Instead of model construction, the goals may be the discovery of individual ...
The output of a hierarchical clustering algorithm is a hierarchical tree or dendrogram. A sample dendrogram is shown in Figure 1-2. A dendrogram is a tree where the initial clusters, consisting of one element only, form the leaves of ...
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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