Introduction to Data Mining

Introduction to Data Mining

• Data Mining is the process of collecting large amounts of data and transforming that data into useful information
• Data mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown, and potentially useful information from the databases.

Fig. Data Mining Discovery Process

Data mining discovery process

1. Data cleaning :
   It is a phase in which noise data and irrelevant data are removed from the collection.
2. Data Integration :
   It is the process in which the multiple data sources, often heterogeneous may be combined in common sources.
3. Data Selection :
   In this process, the relevant data is decided to the analysis and retried from data collection.
4. Data Transformation :
   In this phase, the selected data is transformed into forms that are appropriate for the mining process.
5. Data Mining :
   This is an important step in which the cleverer techniques are applied to extract the patterns potentially useful.
6. Pattern Evaluation :
   In this phase, the good patterns representing knowledge are identified based on given measures.
7. Knowledge Representation :
   This is the last phase where all discovered knowledge is visually represented to the user. This uses Data visualization techniques to help users to understand the results in an easy manner.

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