Tom will introduce the fundamentals of data mining: its applications, the process and the underlying principles, and apply this to data analysis tools.
Using a “magic quadrant” for analytical tool design, Tom will overview a number of open and commercial tools, compare their strengths and weaknesses, and suggest how to place open data analysis tools at the top of the data mining heap.
Bio: Tom Khabaza, sometimes called “the Isaac Newton of Data Mining” is the Founding Chairman of the Society of Data Miners. A data mining veteran of 25 years and many industries and applications, Tom helped create the world-leading Clementine data mining workbench (now called IBM SPSS Modeler) and the industry standard CRISP-DM analytics methodology, and led the first integrations of data mining and text mining. His recent thought leadership includes the 9 Laws of Data Mining and Predictive Analytics Strategy.