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  • Data Mining Algorithms – EM Clustering

    With the K-Means algorithm, each object is assigned to exactly one cluster. It is assigned to this cluster with a probability equal to 1.0. It is assigned to all other clusters with a probability equal to 0.0. This is hard clustering. Instead of distance, you can use a probabilistic measure to determine cluster membership. For example, you can ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on May 12, 2015
  • 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
  • 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
  • Indexing, Querying and Analyzing Text with SQL Server 2012-2014

    It is hard to imagine searching for something on the Web without modern search engines like Bing or Google. However, most contemporary applications still limit users to exact searches only. For end users, even the standard SQL LIKE operator is not powerful enough for approximate searches. In addition, many documents are stored in modern databases; ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on February 7, 2014
  • 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
  • Videos about #DAX on Project Botticelli

    More than one year ago, I and Alberto started recording videos for Project Botticelli, and now we have a set of videos about DAX that you can watch online. There are a few videos free, and others are available in the monthly subscription. If you are interested, use this 20% discount code before the end of the December 2013: SQLBI20HOLS2013 The ...
    Posted to SQLBI - Marco Russo (Weblog) by sqlbi on December 11, 2013
  • 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
  • Fraud Detection with the SQL Server Suite Part 2

    This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on October 29, 2013
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