Database Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery
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Comparison of <strong>Knowledge</strong> <strong>Discovery</strong><br />
Process Models<br />
Model<br />
domain of origin<br />
# steps<br />
Steps<br />
Fayyad et al.<br />
academic<br />
9<br />
1. Developing <strong>and</strong> Underst<strong>and</strong>ing of the<br />
Application Domain<br />
2. Creating a Target Data Set<br />
Cios et al.<br />
hybrid (academic/industry)<br />
6<br />
1. Underst<strong>and</strong>ing of the Problem<br />
Domain<br />
2. Underst<strong>and</strong>ing of the Data<br />
CRISP-DM<br />
industry<br />
6<br />
1. <strong>Business</strong> Underst<strong>and</strong>ing<br />
2. Data Underst<strong>and</strong>ing<br />
Notes<br />
supporting software<br />
3. Data Cleaning <strong>and</strong> Preprocessing<br />
4. Data Reduction <strong>and</strong> Projection<br />
5. Choosing the Data Mining Task<br />
6. Choosing the Data Mining Algorithm<br />
7. Data Mining<br />
8. Interpreting Mined Patterns<br />
9. Consolidating Discovered <strong>Knowledge</strong><br />
the most popular model; provides detailed<br />
technical description with respect to data<br />
analysis, but lacks business aspects<br />
commercial system MineSet TM<br />
3. Preparation of the Data<br />
4. Data Mining<br />
5. Evaluation of the Discovered<br />
<strong>Knowledge</strong><br />
6. Use of the Discovered <strong>Knowledge</strong><br />
draws from both academic <strong>and</strong> industrial<br />
models; emphasizes iterative aspects;<br />
identifies <strong>and</strong> describes explicit<br />
feedback loops<br />
N/A<br />
3. Data Preparation<br />
4. Modeling<br />
5. Evaluation<br />
6. Deployment<br />
uses easy to underst<strong>and</strong><br />
vocabulary; has good<br />
documentation;<br />
commercial system Clementine®<br />
reported application<br />
domains<br />
medicine, engineering, production,<br />
e-business, software<br />
medicine, software<br />
medicine, engineering, marketing,<br />
sales<br />
Engineering <strong>and</strong> Technology Management<br />
34
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