Describe the various functionalities of Data Mining as a step in the process of Knowledge Discovery.

• Knowledge discovery in the database is the process of searching for hidden knowledge in the massive amounts of data that we are technically capable of generating and storing.
• The basic task of KDD is to extract knowledge (or information) from a lower level data (databases).
• It is the non-trivial (significant) process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.
• The goal is to distinguish between unprocessed data something that may not be obvious but is valuable or enlightening in its discovery.
• The overall process of finding and interpreting patterns from data involves the repeated application of the following steps:
1. Data Cleaning
2. Data Integration
3. Data Selection
4. Data Transformation
5. Data Mining
6. Pattern Evaluation
7. Knowledge Presentation
• Steps 1 to 4 are different forms of data preprocessing, where data are prepared for mining.

Fig. Data Mining as a step in the process of knowledge discovery

1. Data Cleaning
• Removal of noise, inconsistent data, and outliers
• Strategies to handle missing data fields.
2. Data Integration
• Data from various sources such as databases, data warehouse, and transactional data are integrated.
• where multiple data sources may be combined into a single data format.
3. Data Selection
• Data relevant to the analysis task is retrieved from the database.
• Collecting only necessary information to the model.
• Finding useful features to represent data depending on the goal of the task.
4. Data Transformation
• Data are transformed and consolidated into forms appropriate for mining by performing summary or aggregation operations.
• By using transformation methods invariant representations for the data is found.
5. Data Mining
• An essential process where intelligent methods are applied to extract data patterns.
• Deciding which model and parameter may be appropriate.
6. Pattern Evaluation
• To identify the truly interesting patterns representing knowledge based on interesting measures.
7. Knowledge Presentation
• Visualization and knowledge representation techniques are used to present mined knowledge to users.
• Visualizations can be in form of graphs, charts or table.