Data mining and knowledge discovery

Data Mining

Data Mining is defined as the procedure of extracting information from huge sets of data. In other words, we can say that data mining is mining knowledge from data. The tutorial starts off with a basic overview and the terminologies involved in data mining and then gradually moves on to cover topics such as knowledge discovery, query language, classification and prediction, decision tree induction, cluster analysis, and how to mine the Web.

Knowledge Discovery

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

knowledge discovery while others view data mining as an essential step in the process of knowledge discovery. Here is the list of steps involved in the knowledge discovery process −

• Data Cleaning − In this step, the noise and inconsistent data is removed.
• Data Integration − In this step, multiple data sources are combined.
• Data Selection − In this step, data relevant to the analysis task are retrieved from the database.
• Data Transformation − In this step, data is transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations.
• Data Mining − In this step, intelligent methods are applied in order to extract data patterns.
• Pattern Evaluation − In this step, data patterns are evaluated.
• Knowledge Presentation − In this step, knowledge is represented.