Data Mining

To give a perspective, there are four main types of data mining tasks: association rule learning, clustering, classification, and regression. We have identified that these types of data mining tasks are useful in each of the research strands discussed in this research proposal.

Here's a more detailed breakdown: 

1. Data Cleaning:

This step involves addressing inconsistencies, missing values, and errors in the data to ensure its quality and accuracy.

2. Data Integration:

Different data sources are combined and harmonized to create a unified view of the data.

3. Data Reduction:

Unnecessary or redundant data is eliminated to improve efficiency and analysis.

4. Data Transformation:

Data is converted into a format suitable for analysis, such as normalization, aggregation, or generalization.

5. Data Mining:

Various algorithms and techniques are applied to the transformed data to discover patterns and relationships.

6. Pattern Analysis:

The identified patterns and relationships are analyzed to understand their significance and implications.

7. Sharing Final Report:

The findings and insights derived from the data mining process are communicated to stakeholders through reports, visualizations, or other forms.