Data Transformation

In data transformation, the data are transformed or consolidated into forms appropriate for mining. Data transformation can involve the following:

• ·         Smoothing, this works to remove noise from the data. Such techniques include binning, regression, and clustering.
• ·         Aggregation, where summary or aggregation operations are applied to the data. For example, the daily sales data may be aggregated so as to compute monthly and annual total amounts. This step is typically used in constructing a data cube for analysis of the data at multiple granularities.
• ·         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. Similarly, values for numerical attributes, like age, may be mapped to higher-level concepts, like youth, middle-aged, and senior.
• ·         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 (or feature construction), where new attributes are constructed and added from the given set of attributes to help the mining process.