Just a reminder that the next Predictive Analytics World is coming in another 6 weeks--Feb 16-17 in San Francisco.
I'll be teaching a pre-conference Hands-On Predictive Analytics workshop using SAS Enterprise Miner on the 15th, and presenting a text mining case study on the 16th.
For any readers here who may be going, feel free to use this discount code during registration to get a 15% discount off the 2-day conference: DEANABBOTT010
Hope to see you there.
Showing posts with label data mining conferences. Show all posts
Showing posts with label data mining conferences. Show all posts
Monday, January 04, 2010
Saturday, January 31, 2009
Predictive Analytics World
There is a new predictive analytics conference coming up Feb 18-19 in San Francisco called Predictive Analytics World. I'm very much looking forward to it in the hopes that it will appeal to the data mining / predictive analytics practitioner.
I'll be presenting a case study I worked on with TN Marketing using ensembles of logistic regression models. Also, I'll be on a panel discussion on Cross-Industry Challenges and Solutions in Predictive Analytics.
Hope to see some of you there!
I'll be presenting a case study I worked on with TN Marketing using ensembles of logistic regression models. Also, I'll be on a panel discussion on Cross-Industry Challenges and Solutions in Predictive Analytics.
Hope to see some of you there!
Monday, October 20, 2008
What topics would you like to see covered at a KDD conference?
This is your chance to voice your opinion!
What topics, sessions, or tutorials would be most useful for you at a conference like KDD? Would a full industrial track be of interest, of are industries so diverse that we really need tracks to be narrowed to specific industries?
Please--practitioners only. I'm defining practitioners as those who get paid to develop models that are actually used in industry.
I'll kick it off with one idea:
Tutorials (1/2 day) geared toward the practitioner. This means that if techniques are described (such as social networking), there must be implementations of the algorithmic ideas available in competitive commercial software. As great as R and Matlab are, for example, relatively few practitioners are programmers that can take advantage of these kinds of frameworks.
I know there are tutorials at KDD every year. This year I didn't go because they were all on Sunday and I wasn't able to attend then, but would have wanted to go to the Text Mining tutorial as that is a topic that has become a significant part of my business over the past couple of years.
One last thought: I think one thing that may happen (understandably) is that topics that have been covered in years passed are not revisited. For those of us who live in the data mining world, it is far more interesting to continue to explore new ideas, especially those that build on ideas we have already explored in depth. However, as data mining increases in its use, we are bringing folks in who have not had that same benefit. For many, a tutorial on decision trees would be very useful and interesting (like the KDD 2001 tutoral--trees to my knowledge have not been revisited since except in the framework of ensembles in 2007).
What topics, sessions, or tutorials would be most useful for you at a conference like KDD? Would a full industrial track be of interest, of are industries so diverse that we really need tracks to be narrowed to specific industries?
Please--practitioners only. I'm defining practitioners as those who get paid to develop models that are actually used in industry.
I'll kick it off with one idea:
Tutorials (1/2 day) geared toward the practitioner. This means that if techniques are described (such as social networking), there must be implementations of the algorithmic ideas available in competitive commercial software. As great as R and Matlab are, for example, relatively few practitioners are programmers that can take advantage of these kinds of frameworks.
I know there are tutorials at KDD every year. This year I didn't go because they were all on Sunday and I wasn't able to attend then, but would have wanted to go to the Text Mining tutorial as that is a topic that has become a significant part of my business over the past couple of years.
One last thought: I think one thing that may happen (understandably) is that topics that have been covered in years passed are not revisited. For those of us who live in the data mining world, it is far more interesting to continue to explore new ideas, especially those that build on ideas we have already explored in depth. However, as data mining increases in its use, we are bringing folks in who have not had that same benefit. For many, a tutorial on decision trees would be very useful and interesting (like the KDD 2001 tutoral--trees to my knowledge have not been revisited since except in the framework of ensembles in 2007).
Thursday, September 25, 2008
KDD 2008
It's hard to believe that KDD2008 was the first KDD I've attended in seven years. It was striking how much has changed in that time, and that was one of the primary reasons I attended this past year--to see for myself if the reports I've heard are true. Sure enough, they are.
These reports, primarily from colleagues in industry, were that KDD didn't have anything they could "take home and use". Many of these folks are analysts who are decidedly not academic, so I thought I had a sense for what they meant.
I found their reports hit the mark. Seven years ago I was able to find (1) significant numbers of industry personnel at the conference and (2) many talks that were accessible enough for non-academics to understand. This time around there were few industry practitioners I met who were not PhDs. That's not to say there weren't interesting talks. Two I didn't see in person, but read later were the Elkan paper on learning from positive and unlabelled examples and the Grossman paper on Data Clouds. Though-provoking both. The lunch talk by Trevor Hastie was very interesting in talking about regularization, but it was geared toward those who can digest his textbook (which is among the finest data mining / statistical learning texts out there).
Social networking was a key theme of the conference, and it was such a dominant force at the conference that it deserves a separate post.
Lastly, the decline in participation by the business community was nowhere more evident than in the vendors room--only a few data mining software vendors were there, which indicates to me that it isn't viewed as a place to increase sales: if I remember correctly, only Microsoft, Oracle, Statsoft, Salford Systems, and SAS were there. A quick look at the kdnuggets software survey shows who wasn't there.
So it seems that KDD has wandered from a business/academic mix to a more academic conference, which is, of course, the prerogative of the organizers. I'm still searching for a great conference for the data mining practitioner who has the level of understanding of data mining to read and absorb a book like the Witten/Frank machine learning book but desires a more practical approach to the subject.
These reports, primarily from colleagues in industry, were that KDD didn't have anything they could "take home and use". Many of these folks are analysts who are decidedly not academic, so I thought I had a sense for what they meant.
I found their reports hit the mark. Seven years ago I was able to find (1) significant numbers of industry personnel at the conference and (2) many talks that were accessible enough for non-academics to understand. This time around there were few industry practitioners I met who were not PhDs. That's not to say there weren't interesting talks. Two I didn't see in person, but read later were the Elkan paper on learning from positive and unlabelled examples and the Grossman paper on Data Clouds. Though-provoking both. The lunch talk by Trevor Hastie was very interesting in talking about regularization, but it was geared toward those who can digest his textbook (which is among the finest data mining / statistical learning texts out there).
Social networking was a key theme of the conference, and it was such a dominant force at the conference that it deserves a separate post.
Lastly, the decline in participation by the business community was nowhere more evident than in the vendors room--only a few data mining software vendors were there, which indicates to me that it isn't viewed as a place to increase sales: if I remember correctly, only Microsoft, Oracle, Statsoft, Salford Systems, and SAS were there. A quick look at the kdnuggets software survey shows who wasn't there.
So it seems that KDD has wandered from a business/academic mix to a more academic conference, which is, of course, the prerogative of the organizers. I'm still searching for a great conference for the data mining practitioner who has the level of understanding of data mining to read and absorb a book like the Witten/Frank machine learning book but desires a more practical approach to the subject.
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