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Analysis of Applicable Data Resources The business objective and DM objective provide a glimpse into the applicable data resources. The DM projects base can also be used to identify applicable data by searching for similar past projects. It is important to note that as business situations change, new variables may be needed based on the set DM objectives. Data on these new variables may be available to the organization or may need to be purchased from an external data vendor.
Output of clustering to be used as input to classification or estimation. If classification: entity to be classified and name of the target variable (types: binary, nominal, ordinal, and interval/ continuous) If estimation: name of the target variable (type: continuous) Output of association rules can be used to drive a directed data mining effort such as classification Association rules: Item Set Classification: entity to be classified and name of the target variable (types: binary, nominal, ordinal, and interval/continuous) Continuing with the example of Global Credit, here is how the DM objective can be formulated: The business objective was set up as improving approval rates of subprime customers by 5%, while maintaining better or similar loss rates.
KDDM Process Models and Examples Several KDDM process models have been proposed (Fayyad 1996; Berry and Linoff 1997; Anand and Buchner 1998; Cabena et al. 1998; Cios et al. 2000; Han and Kamber 2001; CRISP-DM 2003; Cios and Kurgan 2005) that, while differing in their level of detail, describe the same sequence of phases from BU to deployment, to describing the life cycle of a KDDM project. 28 ◾ Sumana Sharma and Kweku-Muata Osei-Bryson Limitations of KDDM Process Models Our analysis of existing KDDM process models reveals that they suffer from certain common deficiencies.
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