DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Sample Applications

The tool is suitable for general application, including finance and quality management.

The typical finance application is loan application processing. Consider a bank receiving an average of over 900 loan applications per day, requiring considerable manpower to process in a timely and accurate manner. A decision support system for loan application screening was developed using STATISTICA Data Miner, where applicant data are processed using a variety of analytic methods, ranging from simple IF-THEN-ELSE decision rules to advanced classification and regression tree models designed to resolve ambiguities. Such preprocessing of each application takes only seconds, and, in some cases, includes verification through automatic queries to a large data warehouse. A risk rating is generated indicating: very low risk, low risk, acceptable risk, high risk, and very high risk. Since the system was deployed the rate of error in the loan approval process has reportedly decreased ``significantly''.

For quality management a semiconductor manufacturer employed a totally automated system for ``Intelligent Quality Monitoring'' (IQM). IQM collects over 50 thousand characteristics of the production process in real-time (ranging from simple temperature variations to scores from multi-perspective laser scanning detection systems). All these data are pre-screened in real-time and fed into an intelligent data processor built on predictive data mining technologies. The risk of producing a defective wafer is calculated and monitored in real-time and the decisions to make costly adjustments or to terminate a wafer burning cycle entirely are made automatically. Moreover, this decision process is integrated with the corporate Enterprise Resource Planning system, and thus linked to data on fluctuating cost considerations. Consistently higher yields per cycle, compared to its competitors, are now achieved, and the cost per unit has been reduced by 27%.

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