Tag: data mining

PMML Plugin for Greenplum now available

Predictive Model Markup Language
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From a press release from Zementis.

 

, the Universal PMML Plug-in for in-database scoring. Available now for the EMC Greenplum Database, a high-performance massively parallel processing (MPP) database, the plug-in leverages the Predictive Model Markup Language (PMML) to execute predictive models directly within EMC Greenplum, for highly optimized in-database scoring.

Universal PMML Plug-in

Developed by the Data Mining Group (DMG), PMML is supported by all major data mining vendors, e.g., IBM SPSS, SAS, Teradata, FICO, STASTICA, Microstrategy, TIBCO and Revolution Analytics as well as open source tools like R, KNIME and RapidMiner. With PMML, models built in any of these data mining tools can now instantly be deployed in the EMC Greenplum database. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides.

“By partnering with Zementis, a true PMML innovator, we are able to offer a vendor-agnostic solution for moving enterprise-level predictive analytics into the database execution environment,” said Dr. Steven Hillion, Vice President of Analytics at EMC Greenplum. “With Zementis and PMML, the de-facto standard for representing data mining models, we are eliminating the need to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today.”

Want to learn more?
 

To learn more about how the EMC Greenplum Database and the Universal PMML Plug-in work together, feel free to:

  1. Visit the PMML Plug-in product page
  2. Download the white paper

The Universal PMML Plug-in for the EMC Greenplum Database is available now. Contact us today for more information.
Michael Zeller, CEO, Zementis

 

 

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KDNuggets Survey on R

CRISP-DM
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From http://www.kdnuggets.com/2011/03/new-poll-r-in-analytics-data-mining-work.html?k11n07

A new poll/survey on actual usage of R in Data Mining

R has been steadily growing in popularity among data miners and analytic professionals.

In KDnuggets 2010 Data Mining / Analytic Tools Poll, R was used by 30% of respondents.
In 2010 Rexer Analytics Data Miner SurveyR was the most popular tool, used by 43% of the data miners.

Another aspect of tool usefulness is how much does it help with the entire data mining process from data preparation and cleaning, modeling, evaluation, visualization and presentation (excluding deployment).

New KDnuggets Poll is asking:
What part of your analytics / data mining work in the past 12 months was done in R?

http://www.kdnuggets.com/2011/03/new-poll-r-in-analytics-data-mining-work.html?k11n07

 

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Heritage Health Prize- Data Mining Contest for 3mill USD

An animation of the quicksort algorithm sortin...
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If Netflix was about 1 mill USD to better online video choices, here is a chance to earn serious money, write great code, and save lives!

From http://www.heritagehealthprize.com/

Heritage Health Prize
Launching April 4

Laptop

More than 71 Million individuals in the United States are admitted to
hospitals each year, according to the latest survey from the American
Hospital Association. Studies have concluded that in 2006 well over
$30 billion was spent on unnecessary hospital admissions. Each of
these unnecessary admissions took away one hospital bed from someone
else who needed it more.

Prize Goal & Participation

The goal of the prize is to develop a predictive algorithm that can identify patients who will be admitted to the hospital within the next year, using historical claims data.

Official registration will open in 2011, after the launch of the prize. At that time, pre-registered teams will be notified to officially register for the competition. Teams must consent to be bound by final competition rules.

Registered teams will develop and test their algorithms. The winning algorithm will be able to predict patients at risk for an unplanned hospital admission with a high rate of accuracy. The first team to reach the accuracy threshold will have their algorithms confirmed by a judging panel. If confirmed, a winner will be declared.

The competition is expected to run for approximately two years. Registration will be open throughout the competition.

Data Sets

Registered teams will be granted access to two separate datasets of de-identified patient claims data for developing and testing algorithms: a training dataset and a quiz/test dataset. The datasets will be comprised of de-identified patient data. The datasets will include:

  • Outpatient encounter data
  • Hospitalization encounter data
  • Medication dispensing claims data, including medications
  • Outpatient laboratory data, including test outcome values

The data for each de-identified patient will be organized into two sections: “Historical Data” and “Admission Data.” Historical Data will represent three years of past claims data. This section of the dataset will be used to predict if that patient is going to be admitted during the Admission Data period. Admission Data represents previous claims data and will contain whether or not a hospital admission occurred for that patient; it will be a binary flag.

DataThe training dataset includes several thousand anonymized patients and will be made available, securely and in full, to any registered team for the purpose of developing effective screening algorithms.

The quiz/test dataset is a smaller set of anonymized patients. Teams will only receive the Historical Data section of these datasets and the two datasets will be mixed together so that teams will not be aware of which de-identified patients are in which set. Teams will make predictions based on these data sets and submit their predictions to HPN through the official Heritage Health Prize web site. HPN will use the Quiz Dataset for the initial assessment of the Team’s algorithms. HPN will evaluate and report back scores to the teams through the prize website’s leader board.

Scores from the final Test Dataset will not be made available to teams until the accuracy thresholds are passed. The test dataset will be used in the final judging and results will be kept hidden. These scores are used to preserve the integrity of scoring and to help validate the predictive algorithms.

Teams can begin developing and testing their algorithms as soon as they are registered and ready. Teams will log onto the official Heritage Health Prize website and submit their predictions online. Comparisons will be run automatically and team accuracy scores will be posted on the leader board. This score will be only on a portion of the predictions submitted (the Quiz Dataset), the additional results will be kept back (the Test Dataset).

Form

Once a team successfully scores above the accuracy thresholds on the online testing (quiz dataset), final judging will occur. There will be three parts to this judging. First, the judges will confirm that the potential winning team’s algorithm accurately predicts patient admissions in the Test Dataset (again, above the thresholds for accuracy).

Next, the judging panel will confirm that the algorithm does not identify patients and use external data sources to derive its predictions. Lastly, the panel will confirm that the team’s algorithm is authentic and derives its predictive power from the datasets, not from hand-coding results to improve scores. If the algorithm meets these three criteria, it will be declared the winner.

Failure to meet any one of these three parts will disqualify the team and the contest will continue. The judges reserve the right to award second and third place prizes if deemed applicable.

 

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HIGHLIGHTS from REXER Survey :R gives best satisfaction

Simple graph showing hierarchical clustering. ...
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A Summary report from Rexer Analytics Annual Survey

 

HIGHLIGHTS from the 4th Annual Data Miner Survey (2010):

 

•   FIELDS & GOALS: Data miners work in a diverse set of fields.  CRM / Marketing has been the #1 field in each of the past four years.  Fittingly, “improving the understanding of customers”, “retaining customers” and other CRM goals are also the goals identified by the most data miners surveyed.

 

•   ALGORITHMS: Decision trees, regression, and cluster analysis continue to form a triad of core algorithms for most data miners.  However, a wide variety of algorithms are being used.  This year, for the first time, the survey asked about Ensemble Models, and 22% of data miners report using them.
A third of data miners currently use text mining and another third plan to in the future.

 

•   MODELS: About one-third of data miners typically build final models with 10 or fewer variables, while about 28% generally construct models with more than 45 variables.

 

•   TOOLS: After a steady rise across the past few years, the open source data mining software R overtook other tools to become the tool used by more data miners (43%) than any other.  STATISTICA, which has also been climbing in the rankings, is selected as the primary data mining tool by the most data miners (18%).  Data miners report using an average of 4.6 software tools overall.  STATISTICA, IBM SPSS Modeler, and R received the strongest satisfaction ratings in both 2010 and 2009.

 

•   TECHNOLOGY: Data Mining most often occurs on a desktop or laptop computer, and frequently the data is stored locally.  Model scoring typically happens using the same software used to develop models.  STATISTICA users are more likely than other tool users to deploy models using PMML.

 

•   CHALLENGES: As in previous years, dirty data, explaining data mining to others, and difficult access to data are the top challenges data miners face.  This year data miners also shared best practices for overcoming these challenges.  The best practices are available online.

 

•   FUTURE: Data miners are optimistic about continued growth in the number of projects they will be conducting, and growth in data mining adoption is the number one “future trend” identified.  There is room to improve:  only 13% of data miners rate their company’s analytic capabilities as “excellent” and only 8% rate their data quality as “very strong”.

 

Please contact us if you have any questions about the attached report or this annual research program.  The 5th Annual Data Miner Survey will be launching next month.  We will email you an invitation to participate.

 

Information about Rexer Analytics is available at www.RexerAnalytics.com. Rexer Analytics continues their impressive journey see http://www.rexeranalytics.com/Clients.html

|My only thought- since most data miners are using multiple tools including free tools as well as paid software, Perhaps a pie chart of market share by revenue and volume would be handy.

Also some ideas on comparing diverse data mining projects by data size, or complexity.

 

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Protected: Whats behind that pretty SAS Blog?

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Interview Anne Milley JMP

Here is an interview with Anne Milley, a notable thought leader in the world of analytics. Anne is now Senior Director, Analytical Strategy in Product Marketing for JMP , the leading data visualization software from the SAS Institute.

Ajay-What do you think are the top 5 unique selling points of JMP compared to other statistical software in its category?

Anne-

JMP combines incredible analytic depth and breadth with interactive data visualization, creating a unique environment optimized for discovery and data-driven innovation.

With an extensible framework using JSL (JMP Scripting Language), and integration with SAS, R, and Excel, JMP becomes your analytic hub.

JMP is accessible to all kinds of users. A novice analyst can dig into an interactive report delivered by a custom JMP application. An engineer looking at his own data can use built-in JMP capabilities to discover patterns, and a developer can write code to extend JMP for herself or others.

State-of-the-art DOE capabilities make it easy for anyone to design and analyze efficient experiments to determine which adjustments will yield the greatest gains in quality or process improvement – before costly changes are made.

Not to mention, JMP products are exceptionally well designed and easy to use. See for yourself and check out the free trial at www.jmp.com.

Download a free 30-day trial of JMP.

Ajay- What are the challenges and opportunities of expanding JMP’s market share? Do you see JMP expanding its conferences globally to engage global audiences?

Anne-

We realized solid global growth in 2010. The release of JMP Pro and JMP Clinical last year along with continuing enhancements to the rest of the JMP family of products (JMP and JMP Genomics) should position us well for another good year.

With the growing interest in analytics as a means to sustained value creation, we have the opportunity to help people along their analytic journey – to get started, take the next step, or adopt new paradigms speeding their time to value. The challenge is doing that as fast as we would like.

We are hiring internationally to offer even more events, training and academic programs globally.

Ajay- What are the current and proposed educational and global academic initiatives of JMP? How can we see more JMP in universities across the world (say India- China etc)?

Anne-

We view colleges and universities both as critical incubators of future JMP users and as places where attitudes about data analysis and statistics are formed. We believe that a positive experience in learning statistics makes a person more likely to eventually want and need a product like JMP.

For most students – and particularly for those in applied disciplines of business, engineering and the sciences – the ability to make a statistics course relevant to their primary area of study fosters a positive experience. Fortunately, there is a trend in statistical education toward a more applied, data-driven approach, and JMP provides a very natural environment for both students and researchers.

Its user-friendly navigation, emphasis on data visualization and easy access to the analytics behind the graphics make JMP a compelling alternative to some of our more traditional competitors.

We’ve seen strong growth in the education markets in the last few years, and JMP is now used in nearly half of the top 200 universities in the US.

Internationally, we are at an earlier stage of market development, but we are currently working with both JMP and SAS country offices and their local academic programs to promote JMP. For example, we are working with members of the JMP China office and faculty at several universities in China to support the use of JMP in the development of a master’s curriculum in Applied Statistics there, touched on in this AMSTAT News article.

Ajay- What future trends do you see for 2011 in this market (say top 5)?

Anne-

Growing complexity of data (text, image, audio…) drives the need for more and better visualization and analysis capabilities to make sense of it all.

More “chief analytics officers” are making better use of analytic talent – people are the most important ingredient for success!

JMP has been on the vanguard of 64-bit development, and users are now catching up with us as 64-bit machines become more common.

Users should demand easy-to-use, exploratory and predictive modeling tools as well as robust tools to experiment and learn to help them make the best decisions on an ongoing basis.

All these factors and more fuel the need for the integration of flexible, extensible tools with popular analytic platforms.

Ajay-You enjoy organic gardening as a hobby. How do you think hobbies and unwind time help people be better professionals?

Anne-

I am lucky to work with so many people who view their work as a hobby. They have other interests too, though, some of which are work-related (statistics is relevant everywhere!). Organic gardening helps me put things in perspective and be present in the moment. More than work defines who you are. You can be passionate about your work as well as passionate about other things. I think it’s important to spend some leisure time in ways that bring you joy and contribute to your overall wellbeing and outlook.

Btw, nice interviews over the past several months—I hadn’t kept up, but will check it out more often!

Biography–  Source- http://www.sas.com/knowledge-exchange/business-analytics/biographies.html

  • Anne Milley

    Anne Milley

    Anne Milley is Senior Director of Analytics Strategy at JMP Product Marketing at SAS. Her ties to SAS began with bank failure prediction at Federal Home Loan Bank Dallas and continued at 7-Eleven Inc. She has authored papers and served on committees for F2006, KDD, SIAM, A2010 and several years of SAS’ annual data mining conference. Milley is a contributing faculty member for the International Institute of Analytics. anne.milley@jmp.com