Abbott Analytics: Data Mining Consulting

Services: Data Mining Project Assessment, Data Preparation For Data Mining, Data Mining Model Development, Data Mining Model Deployment, Data Mining Course: Overview for Project Managers, Data Mining Course: Overview for Practitioners, Customized Data Mining Engagements

Abbott Insights

Insight 1: Find Correlated Variables Prior to Modeling Topic: Data Understanding and Data Preparation Sub-Topic: Feature Selection Insight 2: Beware of Outliers in Computing Correlations Topic: Data Preparation Sub-Topic: Outliers Insight 3: Create Three Sampled Data Sets, not Two Topic: Modeling Sub-Topic: Sampling Insight 4: Use Priors to Balance Class Counts Topic: Modeling Sub-Topic: Decision Trees Insight 5: Beware of Automatic Handling of Categorical Variables Topic: Data Understanding and Data Preparation Sub-Topic: Feature Selection and Creation Insight 6: Gain Insights by Building Models from Several Algorithms Topic: Modeling Sub-Topic: Algorithm Selection Insight 7: Beware of Being Fooled with Model Performance Topic: Data Evaluation Sub-Topic: Model Performance

Data Mining Clients

Client List and Case Studies

Courses and Seminars

Upcoming Data Mining Seminars A Practical Introduction to Data Mining Upcoming courses (nationwide) Data Mining Level II: A drill-down of the data mining process, techniques, and applications Data Mining Level III: A hands-on day of data mining using real data and real data mining software Anytime Courses Overview for Project Managers: Train project managers on the data mining process. Overview for Practitioners: Train practitioners (data analysts, project managers, managers) on the data mining process.

Data Mining Resources

Data Mining Resources, Books, Websites, White Papers, Presentations, Tutorials

About Us

Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including Clementine (SPSS), Affinium Model (Unica Corporation), Model 1 (Group1 Software), and hands-on courses using S-Plus and Insightful Miner (Insightful Corporation), and CART (Salford Systems).

Contact Us


Data Mining Courses and Seminars

Courses and Events

  • Predictive Analytics World Conference - New York, NY: October 23 - 27, 2016
  • Predictive Analytics World Conference - Berlin, Germany: November 7 - 9, 2016
  • TDWI Executive Summit - Austin, TX: December 4 - 9, 2016
  • Follow deanabb on Twitter Follow Dean on Twitter
Data Mining Courses and Seminars from Abbott Analytics

Upcoming Courses and Events (Nationwide)

Predictive Analytics World Conference, New York, NY:

  • Advanced Methods Hands-on: Predictive Modeling Techniques
           Once you know the basics of predictive analytics and have prepared data for modeling, which algorithms should you use? What are the similarities and differences? Which options affect the models most? This workshop dives into the key predictive analytics algorithms for supervised learning,including decision trees, linear and logistic regression, neural networks, k-nearest neighbor, support vector machines, and model ensembles. Attendees will learn "best practices" and attention will be paid to learning and experiencing the influence various options have on predictive models so that attendees will gain a deeper understanding of how the algorithms work qualitatively.
            - New York, NY - October 24, 2016

  • Case Study: SmarterHQ - The Revolution in Retail Customer Intelligence
           In this new era of Big Data, retailers collect data in ever-increasing volume and variety. In the midst of Big Data, a revolution is taking place in how retailers gain insights about customers, whether they interact with the brand online, in stores, or both. This session will describe the transition from reporting to data-driven decisions using predictive analytics. Success requires collecting the right data, creating informative derived attributes, making this data accessible in a timely manner, and building predictive models. Examples, drawn from real-world retailers, will include shopping cart funnel management, shopping cart abandonment, marketing attribution, churn, and purchase propensity.
            - New York, NY - October 25, 2016

  • Q&A: Ask Dean and Karl Anything (about Best Practices for Financial Services and Beyond)
           Preeminent consultant, author and instructor Dean Abbott, along with Karl Rexer, field questions from an audience of predictive analytics practitioners about their work, best practices, and other tips and pointers.
            - New York, NY - October 26, 2016

  • Supercharging Prediction: Hands-On with Ensemble Models
           Are model ensembles an algorithm or an approach? How can one understand the influence of key variables in the ensembles? Which options affect the ensembles most? This workshop dives into the key ensemble approaches including Bagging, Random Forests, and Stochastic Gradient Boosting. Attendees will learn "best practices" and attention will be paid to learning and experiencing the influence various options have on ensemble models so that attendees will gain a deeper understanding of how the algorithms work qualitatively and how one can interpret resulting models. Attendees will also learn how to automate the building of ensembles by changing key parameters.
            - New York, NY - October 27, 2016

  • Predictive Analytics World Conference, Berlin, Germany:

  • Predictive Analytics for Practitioners
           Predictive analytics has moved from a niche technology used in a few industries, to one of the most important technologies any data-driven business needs. Because of the demand, there has been rapid growth in university programs in machine learning and data science. These teach the science well, but do not describe the tradeoffs and the “art” of predictive analytics.
            - Berlin, Germany - November 7, 2016

  • How Predictive Modelers Should Think About Big Data
           Predictive modelers love more data, and oft our mindset is "the more data the better the models" Whether did Means more records or more model inputs. HOWEVER, many times, we find did big data Means big headaches for data preparation and models and worse yet, does not Necessarily result in more accurate models. Rather than focusing on more data, predictive modelers shoulderstand prioritize generating more and better information the models can use to identify the patterns. Motivation can be found from learning how humans process and transform data, and the surprising ways we overcome obstacles and limitations in our abilities to make decisions quickly. This talk will describe how predictive modelers can think about big data and big information, and how big data can be leveraged operationally to allow modelers build data and solutions now in ways unimaginable even five years ago.
            - Berlin, Germany - November 8, 2016

  • Deep Dive - Guided Analytics: Letting the Sexiest Job in the 21st Century Stay Sexy
           Data science has been labeled the sexiest job of the 21st century. The challenge is that there are too few of us and we end up doing many extremely time intensive tasks and not real science, all which is definitely not sexy. But there is an emerging dream: guiding non-data scientists through the process of using machine learning. This is fascinating since Gartner and Forrester say there is potential to reach up to 20x as many people as there are current data scientist. In this presentation, a group of academics has attempted to follow the teachings of Dean Abbott to focus on automating the complex series of data preparation steps needed to do machine learning, then running advanced ensembles and testing, to generate usable Documented results. All while interacting with the casual user who knows their data but not data mining. The results will be presented along with a list of what worked and did not worked. At the end, a panel of experts will talk about the feasibility and practicalities of this approach.
            - Berlin, Germany - November 9, 2016

  • TDWI Executive Summit, Austin, TX:
        An Overview of Predictive Analytics for Practitioners
           Predictive analytics (PA) has emerged as a go-to approach to creating data-driven business decisions. The science of PA is not new nor are the algorithms commonly used in PA. What is new is how organizations are leveraging predictive techniques and insights to drive business value. This tutorial will provide a practitioner’s overview to PA, including popular predictive models and best practices around model implementation. The session will cover the six stages of predictive analytics projects as defined in CRISP-DM, the Cross-Industry Process Model for Data Mining. Each stage is described from the analyst’s perspective, providing insights into what the science tells us about each stage and what art is needed to aid analysts in making good decisions about building actionable predictive models. Throughout the tutorial, concepts will be illustrated with data and real use cases.
            - Austin, TX - December 5, 2016

    Customer On-Site Courses

    Overview for Project Managers: Train project managers on the data mining process.

    Overview for Practitioners: Train practitioners (data analysts, project managers, managers) on the data mining process.

    Past National Courses and Presentations

    Digital Analytics Hub, Monterey, CA:
       Keynote Address: The New Era in Customer Analytics: Big Data, Cloud Computing & Advanced Analytics
           In this new era of Big Data, retailers collect data in ever-increasing volume, variety, and even velocity. In the midst of Big Data, a revolution is taking place in how retailers gain insights about customers, whether they interact with the brand online, in stores, or both.
            - Monterey, CA - September 26 - 28, 2016

    TDWI Data Science Bootcamp, San Diego, CA:
       An Overview of Data Science
           Data science has been called “the sexiest job of the 21st century” and with good reason—the size and breadth of our data is growing exponentially, making our ability to understand that data more and more challenging. This session defines data science, describes how it is similar and different from related analytics disciplines, and the key concepts every data scientist needs to know. In this overview, data science will be described in a project-oriented framework. Each project must define objectives, collect and integrate data, prepare it for analysis, perform the analysis, and deploy the results. Whether the end-goal of the project is reporting, visualization, descriptive modeling, or predictive modeling, the same principles apply. For each stage, key principles will be described and real-world examples will illustrate the meaning of these principles.
            - San Diego, CA - October 3, 2016

    Cloud Analytics Symposium, Los Angeles, CA:        

  • Join industry thought leaders and innovators for an information-packed symposium on the future of analytics and data warehousing, brought to you by Snowflake Computing, Tableau, and Amazon Web Services. You’ll hear perspectives from thought leaders on what companies need to know to evolve and succeed with analytics, complemented by real-world examples of what innovators like PLAYSTUDIOS are doing to take advantage of cloud analytics as well as perspectives from industry leaders Tableau and Snowflake Computing.
            - Event Preview, Online Live - June 10, 2016, 11:00am PT
            - Los Angeles, CA - June 16, 2016

  • KNIME Fall Summit, San Francisco, CA:
           Don't miss the first North America KNIME Summit this September near downtown San Francisco at the Mission Bay Conference Center. For the first time, we are hosting our signature event in the US, bringing together KNIME users, the KNIME community and partners, and those interested in learning more about KNIME.
            - San Francisco, CA - September 14 - 16, 2016

    Webinar: Practical Customer Analytics using Predictive Approaches:        

  • This webinar will describe predictive approaches to common customer analytics task such as predicting likelihood to purchase or expected near-term customer value. Predictive approaches include considerable data cleaning and preparation, building predictive models, and assessing the predictive models. At each stage of the process. Practical tips for accomplishing these tasks will be described with specific "how tos" using Statistica, compromises are inevitably made because of data problems and time pressures to deploy solutions.
            - Online - Thursday, October 8, 2015, noon PDT

  • Webinar: Best Practices for Analyzing Business Data Using Predictive Analytics:        

  • Predictive analytics and data science have become go-to approaches to advanced analytics analyses and decision making with organizations. However, many organizations have little to no experience with these techniques within their organization to build predictive models or assess capabilities of consultants or potential hires. This topic helps the persons responsible for defining your predictive analytics strategy understand the key steps and considerations for typical predictive analytics projects without resorting to buzz-words. High-level concepts and some deep-dive ideas will be explained in ways that business stakeholders and analysts can understand.
            - Online - Wednesday, October 14, 2015, 1:00pm - 2:00pm EST

  • TDWI Executive Summit, San Diego, CA:

  • A Predictive Approach to Retail Customer Intelligence Using Multi-Channel Data
            - San Diego, CA - September 22, 2015

  • TDWI Accelerate Conference, Boston, MA:

  • An Overview of Predictive Analytics for Practitioners
            - Boston, MA - July 19, 2016

  • PASS Business Analytics Conference, San Jose, CA:

  • A Week in the Life of a Data Scientist
           Predictive modeling contains six stages of analysis according to the Cross Industry Standard Process Model for Data Mining (CRISP-DM). I will break this down into three primary tasks for predictive modelers including preparing data, building models, and explaining results. Data preparation often requires skills in SQL, python, or other languages to be able to pull data out of data stores and convert the normalized data into flattened data that the algorithms can use to build models. Modeling requires a qualitative (if not quantitative) understanding of the algorithms, including mathematics or statistics, in order to bulid the effectively. Finally, modelers should know how to explain the results of their findings to other analysts and to decision-makers and stakeholders. The session will walk through the building of a predictive model for a retail appliction: predicting the days to next purchase propensity model.
            - San Jose, CA - May 3, 2016

  • Predictive Analytics for Business
           This is a 2-hour lab session. This session describes the six stages of predictive analytics projects according to the CRISP-DM predictive modeling framework: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Each stage is described from the analyst’s perspective, providing insights into what the science tells us about each stage, and where theory falls short to help us make about how to proceed in building models. After completion of the lab, participants should be able to load data, perform simple data preparation, and create predictive models from modeling data sets. The data preparation steps will include filling missing values and creating dummy variables. Predictive modeling steps will include sampling, building models with decision trees, logistic regression, and neural networks. Even though we are using only the KNIME software, the principles will apply to any workflow-style predictive analytics software package.
            - San Jose, CA - May 4, 2016

  • PASS Business Analytics Conference:
    From learning best practices and developing new connections with peers and experts to walking away with a deeper and broader understanding of today's analytics technologies, you'll find even more real-world insights and examples, how-to guidance, and strategic vision from some of the most knowledgeable and top-rated speakers in the BA industry. If you work every day to give your organization the information it needs to make better data-driven decisions, this conference is for you.
       - Santa Clara, CA April 22 - 24, 2015

    CRM Conference, Keynote Speaker:
                - Lausanne, Switzerland - November 6 - 7, 2014

    All Analytics A2 Radio:
       The Art of Predictive Modeling - August 20, 2014

    University of California, Irvine Extension:
       Algorithms, Modeling Methods, Verification & Validation
            - Summer Quarter 2016: June 27 - September 9, 2016
                - Spring Quarter 2016 - April 4 - May 22, 2016
                - Summer Quarter 2014; July 21 - September 7, 2014
                - Winter Quarter 2014; January 27 - March 16, 2014
       Deploying and Refining Predictive Models
                - Irvine, CA (Online) - May 16 - June 19, 2016

    9th KNIME User Group Meeting and Workshops:        

  • This annual event serves an international forum for discussion of KNIME and how it is used in various fields such as business and customer intelligence, analytics and the life sciences. It brings together practitioners and researchers from around the world to explore KNIME and examine how KNIME is used in different industries.
            - Berlin, Germany - February 24 - 26, 2016

  • 7th KNIME User Group Meeting and Workshops:
               This annual event serves an international forum for discussion of KNIME and how it is used in various fields such as business and customer intelligence, analytics and the life sciences. It brings together practitioners and researchers from around the world to explore KNIME and examine how KNIME is used in different industries.
                - Zurich, Switzerland February 12 - 13, 2014

    1st San Diego KNIME Meet-Up:
               This is the inaugural San Diego KNIME Meetup. We will recap the highlights from the KNIME User Group Meeting in Zurich earlier that month and we will also have a couple of user presentations from the area. There will be plenty of time for questions, discussions and informal chats over small bites and drinks.
                - San Diego, CA February 26, 2014

    Webinar, Hosted by EITA Global:
       Key Steps in Starting Your First Predictive Analytics Project
               This webinar summarizes the best practices for building predictive models including both the art and the science of PA during each stage of the process, including Data Understanding, Data Preparation, Predictive Modeling and Deployment.
                - January 14, 2014; 10:00am PST (1:00pm EST)

    IBM SPSS Modeler Seminar Series, Hosted by Quebit:
       Techniques for Unbalanced Data
               Unbalanced data is a common problem in data mining – the imbalance of sample size of categories in a target variable. The standard way of addressing the problem is to discard cases using the balance node. But is this the only way? This seminar will discuss the pros and cons of a variety of methods of addressing the problem using Modeler. The balance node will be briefly reviewed, but the focus of the seminar will be the many alternatives to using the balance node in the standard way. Creative ways of using the balance node will be discussed as way as well as techniques like adjusting priors and utilizing costs. In addition there will be a one-hour question and answer follow-up session to address any student questions or concerns.
                - January 22, 2014; 1;00pm - 4:00pm EST
                - January 23, 2014; 1;00pm - 2:00pm EST; Q & A Follow Up

    Talent Analytics Webinar:
       Modeling Analytics Dream Team
               - September 11, 2013 (12:00 - 1:00pm EST)
               Dean will discuss his extensive experience as an analytics professional, building successful analytics teams, working with clients to build their analytics teams, being part of an extended analytics team inside an organization as well as with Elder Research during its early years.

    The Briefing Room:
       Bridging the Gap: Analyzing Data in and Below the Cloud
               - Tuesday, July 23, 2013 - 4:00pm ET
               Today's desire for analytics extends well beyond the traditional domain of Business Intelligence. That's partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets. Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics.

    ISACA San Diego Chapter Meeting:
       Predictive Data Analytics and Fraud Detection
               - San Diego, CA - June 20, 2013

    Data-Intensive Summer School:
       Introduction to Text Mining (KNIME Installation, Data and workflow instructions)
               - San Diego, CA - June 10, 2013

    Fact-Based Performance Management 2013:
       Next Generation Performance Management: Moving from Business Intelligence to Predictive Analytics
               - San Diego, CA - June 6, 2013
               Create a rock solid foundation to advance performance management and grow your analytics practice

    Southern California Chapter of the Marketing Research:
             - Anaheim, CA - May 2, 2013

    INFORMS Conference on Business Analytics & Operations:
             - San Antonio, TX - April 7 - 9, 2013

    PACE (Predictive Analytics Center of Excellence) Data Mining Boot Camp 2:
       Overview of Text Mining
             - San Diego, CA - February 8, 2013
             - San Diego, CA - October 18, 2013

    Pace Tech Talk: Why Model Ensembles Win Data Mining Competitions
    The most effective approach to win predictive analytics data competitions and producing highly accurate predictive models is the use of model ensembles, a technique that combines predictions from multiple models into a single score. The use of ensembles has revolutionized predictive modeling not just in competitions, such as the Netflix Prize, Kaggle, and PAKDD competitions, but also in everyday modeling. This talk introduces model ensembles and will walk through the history of model ensembles in machine learning and predictive analytics, including Bagging, Boosting, Random Forests, Stochastic Gradient Boosting, and heterogenous ensembles. While ensembles appear to be more complex than individual models, thus violating Occams Razor, this talk will explain how to unravel the apparent contradiction. Real-world examples of the application of model ensembles will be provided throughout the talk.
        - San Diego, CA - November 14, 2012

    Joint Statistical Meetings (JSM): Moderator
    Senior executives of major predictive analytics and data mining software firms come together for a panel discussion. Data miners and analysts can hear firsthand the perspectives of the founders, CEO's, and other senior executives at Frontline Systems, Revolution Analytics (R), Rapid-I, Salford Systems, SAS, Statsoft. "Big data" is a rapidly evolving field - so what are these key decision-makers hearing from customers? What are their own plans? Where do they think predictive analytics is headed? What will surprise us? The format will be a dynamic panel-style question/answer session, moderated by industry specialist Dean Abbott (noted for his vendor-neutral workshops on data mining). Audience questions are accepted in advance will be worked into the program.
           - San Diego, CA - August 1, 2012

    University of California, San Diego Extension: Text Mining
    With experts claiming that unstructured data comprises more than 80% of the stored business information, text mining has emerged as a critical leading-edge technology. This course will describe practical techniques for text extraction and text mining in a data mining context, including document clustering and classification, information retrieval and the enhancement of structured data. An emphasis on practical use of text mining in a business context will be evident throughout.
              - May 11, 18, & 25, 2012; 9:00am - 4:00pm
              - February 3, 10, & 17, 2012 9:00am - 4:00pm
              - Spring Quarter 2014; April 28 - June 2, 2014
              - Fall Quarter 2014; September 29 - November 10, 2014

    Text Analytics World:
             Case Study: Rules Rule: Inductive Business-Role Discovery in Text Mining
               - San Francisco, CA - March 7, 2012
             Customer Support Case Study: A Fortune 500 global technology company
             Rules Rule: Inductive Business-Rule Discovery in Text Mining
               - New York City, NY - October 19, 2011

    Data Mining Conference at a Fortune 500 Company: Speaker / Expert Panelist
           - San Diego, CA - May 30, 2012

    SAS Customer Connection for Data Mining: Speaker / Expert Panelist
           - Cary, NC - June 4 - 6, 2012

    JMP Live Webcast: Analytically Speaking
    Many people ask Dean Abbott, President of Abbott Analytics, Inc., how to succeed in data mining and predictive analytics. Now you can, too. We've invited Abbott, a noted data mining expert with more than two decades of experience, to lead us in a conversation about applying advanced data mining, data preparation and data visualization methods to real-world, data-intensive problems.
           - On-line - June 6, 2012; 7:00am Pacific, 10:00am Eastern

    Decision Management Solutions Webinar:
        - Ten Best Practices in Operational Analytics - February 9, 2011

    Predictive Analytics World Government:
             Defense Finance & Accounting Service (DFAS) Case Study
               - Washington, DC - September 12 - 13, 2011

    DM Radio:
        - Back to Basics Best Practices for Data Mining - September 19, 2013
        - Rules, Rules, Rules: The Magic of Real-Time Decisioning - November 29, 2012
        - Acres of Diamonds -- Mining Enterprise Data with an Open Mind - April 5, 2012
        - The Power of Prescience: Achieving Lift with Predictive Analytics - February 24, 2011
        - Stop Thief! Fraud Detection in a Web-Enabled World - September 9, 2010
        - Embedded Analytics and Business Rules: The Holy Grail? - June 3, 2010
        - Text Analytics in the Contextual Enterprise - September 17, 2009
        - Putting the Context Around Text Mining - April 17, 2008

    ACM Data Mining Camp: Expert Panelist
       Focus is on Data Mining, Analytics, Cloud Computing, Machine Learning, and the various applications of these technologies.
           - November 13, 2010

    Predictive Analytics World Conference:
       Keynote Speaker
            - Berlin, Germany - November 3 - 4, 2015
            - London, UK - October 28 - 29, 2015
            - Boston, MA - September 29, 2015
            - London, England - October 29 - 30, 2014
            - Berlin, Germany - November 4 - 5, 2014
            - Berlin, Germany - November 4 - 5, 2013
       Session: Ask Dean Anything (About Best Practices)
            - Chicago, IL - June 22, 2016
            - Boston, MA - September 29, 2015
       Data Preparation from the Trenches: Four Approaches to Deriving Attributes
            - Boston, MA - October 7, 2014
       Supercharging Prediction: Hands-On with Ensemble Models
            - Chicago, IL - June 23, 2016
            - Boston, MA - September 30, 2015
            - Chicago, IL - June 11, 2015
            - San Francisco, CA - April 2, 2015
            - Boston, MA - October 8, 2014
            - San Francisco, CA - March 19, 2014
            - San Francisco, CA - April 17, 2013
       My Five Predictive Analytics Pet Peeves
            - Toronto, ON - May 14, 2014
            - Chicago, IL - June 12, 2013
            - San Francisco, CA - April 16, 2013
            - Toronto, ON - March 20, 2013
       What is Big Data Analytics: A Canadian Perspective
            - Toronto, ON - March 21, 2013
       Workshop: Advanced Methods Hands-On: Predictive Modeling Techniques
            - Chicago, IL - June 20, 2016
            - Boston, MA - October 1, 2015
            - Chicago, IL - June 8, 2015
            - San Francisco, CA - March 30, 2015
            - Boston, MA - October 9, 2014
            - Toronto, ON - May 12, 2014
            - San Francisco, CA - March 20, 2014
            - Chicago, IL - June 14, 2013
            - San Francisco, CA - April 18, 2013
            - Toronto, ON - March 18, 2013
            - Boston, MA - October 4, 2012
            - Chicago, IL - June 28, 2012
            - Toronto, ON - April 27, 2012
            - San Francisco, CA - March 8, 2012
       Full-Day Workshop: Hands-On Predictive Analytics
            - New York, NY - October 18, 2011
            - San Francisco, CA - March 17, 2011
            - Washington, DC - October 18, 2010
            - San Francisco, CA - February 16 - 17, 2010
            - Washington, DC - October 20 - 21, 2009
       Case Study: Hiring and Selecting Key Personnel Using Predictive Analytics
            - Chicago, IL - June 26, 2012
            - Toronto, ON - April 26, 2012
       Case Study: YMCA - Turning Member Satisfaction Surveys into an Actionable Narrative
            - New York, NY - October 19, 2011
            - San Francisco, CA - March 14, 2011
            - Washington, DC - October 19, 2010
       How to Improve Customer Acquisition Models with Ensembles and
       Cross-Industry Challenges and Solutions in Predictive Analytics
            - San Francisco, CA - February 18 & 19, 2009
       Case Study: SmarterHQ - The Revolution in Retail Customer Intelligence
            - Chicago, IL - June 21, 2016

    eMetrics Marketing Optimization Summit:
           - Predicting the Future New York, NY - October 21, 2011
           - Behavioral Driven Marketing Attribution San Jose, CA - May 3 - 7, 2010

    The Modeling Agency Webinar:
        - Failure to Launch: How to get Predictive Analytics off the Ground and into Orbit - March 16, 2010

    StatSoft Webinar:
        - Wisdom of Crowds: Using Ensembles of Predictive Models - February 24, 2010

    Salford Data Mining Conference:
        - A More Transparent Interpretation of Health Club Surveys
          San Diego, CA - May 24, 2012
        - A Business-Centric Solution to Text Mining of Help Desk Data using CART
          San Diego, CA - August 23 - 25, 2009

    TDWI World Conference: Data Mining Techniques, Tools, and Tactics
        - Las Vegas, NV - February 22 - 27, 2009

    Invoice Fraud Detection: Successful application of data mining by Abbott Analytics

    PAW - New York, NY: October 23 - 27, 2016
    PAW - Berlin, Germany: November 7 - 10, 2016
    TDWI Executive Summit - Austin, TX: December 5 - 9, 2016

    Abbott, D.W., H. Vafaie, M. Hutchins, and D. Riney, Improper Payment Detection in Department of Defense Financial Transactions (PDF - 320 KB), Federal Data Mining Symposium, Washington, DC, March 28-29, 2000.