Nine Laws of Data Mining by Tom Khabaza

Nine Laws of Data Mining by Tom Khabaza

1st Law of Data Mining – “Business Goals Law”:

Business objectives are the origin of every data mining solution

2nd Law of Data Mining – “Business Knowledge Law”:
Business knowledge is central to every step of the data mining process

3rd Law of Data Mining – “Data Preparation Law”:

Data preparation is more than half of every data mining process

4th Law of Data Mining – “NFL-DM”:

The right model for a given application can only be discovered by experiment

or “There is No Free Lunch for the Data Miner”

5th Law of Data Mining – “Watkins’ Law”: There are always patterns

6th Law of Data Mining – “Insight Law”:
Data mining amplifies perception in the business domain

7th Law of Data Mining – “Prediction Law”:
Prediction increases information locally by generalisation

8th Law of Data Mining – “Value Law”:

The value of data mining results is not determined by the accuracy or stability
of predictive models

9th Law of Data Mining – “Law of Change”: All patterns are subject to change