DMCT-NOTES/unit 1/06_Data_Discretization.md

Data Discretization

Data Discretization is the process of converting a large number of continuous values into a smaller number of finite intervals (bins).

Why use it?

• Makes data easier to understand.
• Many algorithms work better with categories than infinite numbers.

Techniques

1. Binning

• Sorting data and dividing it into "bins".
• Example: Grouping ages into [0-10], [11-20], etc.
• Helps smooth out noise.

2. Histogram Analysis

• Using a bar chart (histogram) to see the distribution and decide where to split the data.

3. Cluster Analysis

• Using clustering (like K-Means) to group similar values, then using those groups as the intervals.

Concept Hierarchy

• Organizing data from low-level concepts to high-level concepts.
• Example (Location):
  • Street -> City -> State -> Country.
• Top-down Mapping: General to Specific.
• Bottom-up Mapping: Specific to General.