Abbott Analytics: Data Mining Consulting
Services

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).

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Data Mining Resources: Free Data Mining and Statistics Software
  • WEKA
    Machine learning algorithms in Java. Described in Witten/Frank book "Machine Learning." Extensive algorithms and data manipulation, including iconic interface (a la Clementine, Insightful Miner, Enterprise Miner). However, I find the interface a bit odd.
  • ARC Statistics Software
    Stats, scatterplot matrices. Great stats and visualization package.
  • Stuttgart Neural Network Simulator
    18+ NNs, including MLP, RBF, Recurrent, ART, Kohonen SOM. Comprehensive, but requires X Windows server software (to run under MS Windows).
  • LNKnet
    MIT Lincoln Labs; 16 Sup., 4 Unsup. Algs incl. NN, RBF, EM; Linux; Haven't used these enough to provide opinion yet.
  • Octave
    A matrix manipulation environment mostly compatible with Matlab.
  • Random Forests
    The latest (hot!) algorithm from Leo Breiman (command line/Fortran, with GUI also available).
  • MLC++ Class Libraries
    Used to build SGI Mineset.
  • R
    Free Splus Clone.
  • CLUSTO
    Clustering.
  • C4.5
    The predecessor to C5 decision trees.