Based on the popular 9 Laws of Data Mining series by Society of Data Miners President Tom Khabaza, this session will use examples of real projects to introduce you to the art and science 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 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 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 describes how data mining is performed, it does not explain what data mining is or why the process has the properties that it does. This session discusses nine laws of data mining (most of which are well-known to practitioners), as the start of a theory to explain (and not merely describe) the data mining process.
Duncan has been a data miner since the mid 1990s. He was Director of Advanced Analytics at Teradata until 2010, leaving to become Data Director of Experian UK. He rejoined Teradata to lead their European Data Science team.
At Teradata he has been responsible for developing analytical solutions across a number of industries, including warranty and root cause analysis in manufacturing, and social network analysis in telecommunications. These solutions have been developed directly with customers and have been deployed against some of the largest consumer bases in Europe.
In his spare time Duncan has been a city Councillor, chair of a national charity, founded an award winning farmers’ market, and is one of the founding Directors of the Society of Data Miners.
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