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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Pattern recognition and soft computing typically provide techniques for classifying data items. - Text and web mining are used for web page analysis; text categorization; the acquisition, filtering and structuring of textual information ...
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.
Data mining projects were initially carried out in many different ways with each data analyst finding his/her own way of approaching the problem often through trial-anderror. As the data mining techniques and business evolved, ...
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.
As opposed to decision tree and rule set induction, such a question is best addressed by association rule learning techniques, which is an unsupervised learning method, with no class labels assigned to the examples.
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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