KNOWLEDGE DISCOVERY IN DATABASE

Knowledge discovery in databases:

Knowledge discovery is defined as ``the non-trivial extraction of implicit, unknown, and potentially useful information from data''.

The term Knowledge Discovery in Databases, or KDD for short, refers to the broad process of finding knowledge in data, and emphasizes the "high-level" application of particular data mining methods.

It is of interest to researchers in machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, and data visualization.

The unifying goal of the KDD process is to extract knowledge from data in the context of large databases.

Steps involved in knowledge discovery process:

1. Data Cleaning/Preprocessing - In this step the noise and inconsistent data is removed.
2. Data Integration - In this step multiple data sources are combined.
3. Data Selection - In this step data relevant to the analysis task are retrieved from the database (in this step, significant data gets created or selected.)
4. Data Transformation - In this step data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations.
5. Data Mining - In this step intelligent methods are applied in order to extract data patterns.
6. Pattern Evaluation - In this step, data patterns are evaluated.
7. Knowledge Presentation - In this step, knowledge is represented.