DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Incremental or Online Modelling

The modelling approaches we have discussed here are what we might think of as batch learners. The model builders take a training dataset to build a model that might then be deployed. If we want to update the model then the process begins again from a new training dataset, in batch mode, to build a new model.

An alternative paradigm is to incrementally build a model. Such approaches are also often referred to as online model building.

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