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KDnuggets Home » News :: 2013 :: Aug :: Publications :: Catching up with Gregory Piatetsky-Shapiro ( 13:n22 )

Catching up with Gregory Piatetsky-Shapiro

BigML interview with me on how the knowledge discovery field has changed since KDnuggets launch in 1997, the relationship between data mining and machine learning communities, and more.

BigML blog, by andrewshikiar on August 22, 2013.

Gregory Piatetsky-ShapiroGregory Piatetsky-Shapiro is the founder of KDnuggets, which is a leading resource on business analytics, big data, data mining, and data science. The KD in KDnuggets stands for “Knowledge Discovery,” and Gregory is a foremost expert on the subject …

As the fields of knowledge discovery, data mining and machine learning continue to evolve and overlap, we thought it would be interesting to connect with Gregory to get his thoughts and perspective on key trends in the marketplace through a brief Q&A:

BigML: The “KD” in KDnuggets stands for “Knowledge Discovery” – how has knowledge discovery changed since your launch of KDnuggets in 1997? How has hype around big data impacted knowledge discovery?

Gregory Piatetsky-Shapiro (GPS): The term Knowledge Discovery in Data, or KDD, was adopted in the scientific community, and is now part of the names of several conferences, including KDD (the leading research conference in this area, US-based), ECML/PKDD – European-based conference, PAKDD – Pacific/Asia -based conference, the ACM Transactions on Knowledge Discovery in Data (TKDD), and others. However, the term “Knowledge Discovery” did not catch up in the business world, where “Data Mining” was much more popular (1996-2005) , and then it was supplanted by “Analytics” starting in 2006, and now the hottest term is “Big Data” in the popular press and “Data Science” among researchers. At the last KDD conference in Chicago, most attendees, including me, referred to themselves as data scientists, not “data miners” or “knowledge discoverers”.

BigML: In the past there’s been some acrimony between the data mining and machine learning communities. Knowledge discovery seems to be a common goal of both approaches – how do you see the two approaches contributing to knowledge discovery?

GPS: I see that the KDD / data mining / data science community is now more interested in working with big data / HPC, statistics and optimization communities as they have additional tools and methods to contribute. I don’t really see any hostility between machine learning and data mining. A big part of machine learning is interactive – learning for robots, cars, etc. that make dynamic decisions – those researchers deal with a separate class of problems. Machine learning on static data deals with essentially the same problems as data mining and the latest breakthroughs like deep learning are used in ML and KDD communities.

Here is the rest of the interview.

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