Data transformation

Data transformation operations, such as normalization and aggregation, are additional data preprocessing procedures that would contribute toward the success of the mining process.

Data transformation can be achieved in following ways
Smoothing: which works to remove noise from the data
Aggregation: where summary or aggregation operations are applied to the data. For example, the daily sales data may be aggregated so as to compute weekly and annuual total scores.
Generalization of the data: where low-level or “primitive” (raw) data are replaced by higher-level concepts through the use of concept hierarchies. For example, categorical attributes, like street, can be generalized to higher-level concepts, like city or country.
Normalization: where the attribute data are scaled so as to fall within a small specified range, such as −1.0 to 1.0, or 0.0 to 1.0.
Attribute construction : this is where new attributes are constructed and added from the given set of attributes to help the mining process.