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  • Data Mining Algorithms – Support Vector Machines

    Support vector machines are both, unsupervised and supervised learning models for classification and regression analysis (supervised) and for anomaly detection (unsupervised). Given a set of training examples, each marked as belonging to one of categories, an SVM training algorithm builds a model that assigns new examples into one category. An SVM ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on June 23, 2015
  • Data Mining Algorithms – an Introduction

    Data mining is the most advanced part of business intelligence. With statistical and other mathematical algorithms, you can automatically discover patterns and rules in your data that are hard to notice with on-line analytical processing and reporting. However, you need to thoroughly understand how the data mining algorithms work in order to ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on February 19, 2015
  • Fraud Detection with the SQL Server Suite Part 4

    This is the fourth part of the fraud detection whitepaper. You can find the first part, the second part, and the third part in my previous blog posts about this topic. Data Mining Models We create multiple mining models by using different algorithms, different input data sets, and different algorithm parameters. Then we evaluate the models in ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on December 10, 2013
  • Fraud Detection with the SQL Server Suite Part 1

    While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on October 15, 2013
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