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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... decision tree learning techniques , which are mostly concerned with the discovery of classificatory properties of data tables . Data represented in tables may be collected typically from measurements or acquired from clients . Rows in ...
... 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 nodes and arcs . In general , a node is labeled by an attribute ...
... 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 ...
... 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 ...
... 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 the tree . Each internal node represents a cluster that is formed ...
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 |