Knowledge discovery in data mining is the process of finding useful patterns, relationships, or insights from large sets of data. There are two main types: Directed and Undirected knowledge discovery.
Directed knowledge discovery is like aiming for a target or specific answer, while undirected knowledge discovery is more about exploring and finding interesting connections without a specific goal in mind.
Purpose: This type is like having a specific goal in mind. We want to understand how one thing relates to others. (Goal Oriented).
Method: We start with a question or a hypothesis about the data. Then, we use algorithms to analyze the data and find answers or predictions.
Example:
Purpose: Here, we're not looking for specific answers. Instead, we're searching for interesting patterns in the data, even if we don't have a particular question in mind. (Non target field)
Method: We use techniques like clustering or grouping to find similarities or connections between different pieces of data.
Example: