Knowledge discovery in database is the process of finding knowledge in data through the process of data mining. Stages involved in KDD are explained as follows:
Data Selection & Cleansing:
Data analysis and cleansing are essential first steps towards managing the quality of data. The Data Cleanser detects and corrects invalid or inaccurate records based on rules defined to provide a clean and consistent data set. Cleansing of data ensure that the data obtained is standard across all records, and it does not contain invalid values, and that it is formatted correctly.
Data Integration:
Second step of KDD is data integration in which data from various sources are combined. Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated
Selection:
Here, the closely related data to subject area are selected for analysis.
Transformation:
In transformation, a series of rules or functions are applied to the data extracted from the source. If the data source is good, its data may require very less transformation and validation. But the data from some sources might require one or more transformation types to meet the operational needs and make data fit.
Data mining:
In Data Mining, data mining methods (algorithms) are applied in order to extract data patterns.
Various data mining functions are used based on the requirement. such functions can be classification, regression, association etc.
pattern evaluation:
In Pattern Evaluation, data patterns are identified based on some interesting measures and redundant pattern are removed.
Knowledge:
Knowledge is presented to user using one of the various knowledge representation techniques.
Data analysis and cleansing are essential first steps towards managing the quality of data. The Data Cleanser detects and corrects invalid or inaccurate records based on rules defined to provide a clean and consistent data set. Cleansing of data ensure that the data obtained is standard across all records, and it does not contain invalid values, and that it is formatted correctly.
Data Integration:
Second step of KDD is data integration in which data from various sources are combined. Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated
Selection:
Here, the closely related data to subject area are selected for analysis.
Transformation:
In transformation, a series of rules or functions are applied to the data extracted from the source. If the data source is good, its data may require very less transformation and validation. But the data from some sources might require one or more transformation types to meet the operational needs and make data fit.
Data mining:
In Data Mining, data mining methods (algorithms) are applied in order to extract data patterns.
Various data mining functions are used based on the requirement. such functions can be classification, regression, association etc.
pattern evaluation:
In Pattern Evaluation, data patterns are identified based on some interesting measures and redundant pattern are removed.
Knowledge:
Knowledge is presented to user using one of the various knowledge representation techniques.
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