Page 49 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062949lossy compression, with progressive refinement, sometimes small fragments of signal can be reconstructed without reconstructing the whole c-Time sequence: is not audio and typically short and vary slowly with time. d- Dimensionality and numerosity reduction: may also be considered as forms of data compression VI. Data Transformation and Data Discretization Data Transformation: A function that maps the entire set of values of a given attribute to a new set of replacement values, each old value can be identified with one of the new values Methods of data transformation: 1. Smoothing: which works to remove noise from the data. Techniques include binning, regression, and clustering. 2. Attribute construction (or feature construction): where new attributes are constructed and added from the given set of attributes to help the mining process. 3. 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 data analysis at multiple abstraction levels. 4. Normalization: where the attribute data are scaled so as to fall within a smaller range, such as %u22121.0 to 1.0, or 0.0 to 1.0. 5. Discretization, where the raw values of a numeric attribute (e.g., age) are replaced by interval labels (e.g., 0%u201310, 11%u201320, etc.) or conceptual labels (e.g., youth, adult, senior). The labels, in turn, can be recursively organized into higher-level concepts, resulting in a concept hierarchy for the numeric attribute. 6. Concept hierarchy generation for nominal data: where attributes such as street can be generalized to higher-level concepts, like city or country. Many hierarchies for nominal attributes are implicit within the database schema and can be automatically defined at the schema definition level. Normalizing the data attempts to give all attributes an equal weight. Normalization is particularly useful for classification algorithms involving