Fayyad, Usama Microsoft Research, Redmond, Washington.
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- KDD process
- Density estimation and model selection
- Client-server decomposition
- Predictive modeling
- Data summarization
- Dependency modeling
- Change and deviation detection
- Links to Primary Literature
- Additional Readings
The development of computational algorithms for the identification or extraction of patterns (structure) from large data sets. This is done in order to help reduce, model, understand, or analyze the data (Fig. 1). Tasks supported by data mining include prediction, segmentation, dependency modeling, summarization, and change and deviation detection. Database systems have brought digital data capture and storage to the mainstream of data processing, leading to the creation of large data warehouses. These are databases whose primary purpose is to gain access to data for analysis and decision support. Traditional manual data analysis and exploration requires highly trained data analysts and is ineffective for high dimensionality (large numbers of variables) and massive data sets. See also: Algorithm; Big data; Database management system; Data warehouse
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