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Nine Laws of Data Mining

Data mining is the creation of new knowledge in natural or artificial form, by using business knowledge to discover and interpret patterns in data.

In its current form, data mining as a field of practise came into existence in the 1990s, aided by the emergence of data mining algorithms packaged within workbenches so as to be suitable for business analysts.  Perhaps because of its origins in practice rather than in theory, relatively little attention has been paid to understanding the nature of the data mining process.  The development of the CRISP-DM(#ijsrd) methodology in the late 1990s was a substantial step towards a standardised description of the process that had already been found successful and was (and is) followed by most practising data miners.

 Although CRISP-DM(#ijsrd) describes how data mining is performed, it does not explain what data mining is or why the process has the properties that it does.  In this paper I propose nine maxims or “laws” of data mining (most of which are well-known to practitioners), together with explanations where known.  This provides the start of a theory to explain (and not merely describe) the data mining process.

 It is not my purpose to criticise CRISP-DM(#ijsrd); many of the concepts introduced by CRISP-DM(#ijsrd) are crucial to the understanding of data mining outlined here, and I also depend on CRISP-DM’s(#ijsrd) common terminology.  This is merely the next step in the process that started with CRISP-DM(#ijsrd).

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 generalization

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

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