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  • Data Mining Algorithms – K-Means Clustering

    Hierarchical clustering could be very useful because it is easy to see the optimal number of clusters in a dendrogram and because the dendrogram visualizes the clusters and the process of building of that clusters. However, hierarchical methods don’t scale well. Just imagine how cluttered a dendrogram would be if 10,000 cases would be shown on ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on April 17, 2015
  • Data Mining Algorithms – Hierarchical Clustering

    Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. There are a large number of clustering algorithms. The ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 28, 2015
  • Fraud Detection with the SQL Server Suite Part 5

    This is the fifth, the final part of the fraud detection whitepaper. You can find the first part, the second part, the third part, and the fourth part in my previous blog posts about this topic. The Results In my original fraud detection whitepaper I wrote for SolidQ, I was advised by my friends to include some concrete and simple numbers to ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on January 6, 2014
  • 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 3

    This is the third part of the fraud detection whitepaper. You can find the first part and the second part in my previous blog posts about this topic. Data Preparation The problem of credit card fraud detection is not trivial. With every transaction processed, only a limited amount of data is available, making it difficult if not impossible to ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on November 27, 2013
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