1. Knowledge Discovery and Data Mining: Know What You are looking For Knowledge discovery process Using knowledge discovered Data mining

2. Knowledge Discovery Process ( 20% 80% time vs importance) IExploring the problem space IIExploring the solution space IIISpecifying the implementation strategy IVMining the data –preparing the data (fixing data to meet needs) –surveying the data (zoom in and locate likely) –modeling the data (applying tool on the data to get what we want to know)

3. Outcome of a Data Mining Project The expectation of magic: “Let me give you my data, tell me what you find.” The outcome of a data mining project consist of a model which does one of 2 things: –explanatory (help explain relationships in data) –predictive (make predictions of output conditions given a set of input conditions)

4. Actionable Information Whether explanatory or predictive, the data mining model must provide actionable information -- key to success. The purpose is to provide information that will allow better decision-making. Data mining is a tool in decision support arsenal. This is a structured search of alternatives and options.

5. Summary Solving the right problem is more important than simply building a good model. A data mining project will not be successful if it is not driven by business/research needs. Domain experts involvement was crucial to finding appropriate problem and solution.