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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 dont 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
  • re: Data Mining Algorithms – Hierarchical Clustering

    Hierarchical clustering can use any kind of a distance; in fact, it does not need the original cases once the distance matrix is built. Therefore, you can use a distance that takes into account correlations, like the Mahalanobis distance (http://en.wikipedia.org/wiki/Mahalanobis_distance). MS supports K-Means and Expectation-Maximization ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 30, 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
  • re: Data Mining Algorithms – Association Rules

    Kevin, First of all, thank you for your kind comment. In SQL, you typically search for distinct combinations of items in the same transaction with either join or apply operator. I prefer apply. Bellow is an example that finds itemsets of size 1, 2, and 3. However, I would not recommend doing this in SQL - why would you reinvent the wheel? You ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 25, 2015
  • Data Mining Algorithms – Association Rules

    The Association Rules algorithm is specifically designed for use in market basket analyses. This knowledge can additionally help in identifying cross-selling opportunities and in arranging attractive packages of products. This is the most popular algorithm used in web sales. You can even include additional discrete input variables and predict ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 10, 2015
  • re: SQL Server 2014 Upgrade Technical Guide

    When I click the link from the post, it drives me directly to the 2014 guide and opens a PDF. Are you using the same link?
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 2, 2015
  • re: Conferences 2015 Q1 and Q2

    And we started in Vienna:-)
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 1, 2015
  • Conferences 2015 Q1 and Q2

    In two days, I am starting my first conference trip for this year. Therefore, it seems to me it is high time to write down my plan for the first semester of this year. Of course, I m adding my food plan for each event:-) SQL Saturday #374 Vienna. On Friday, February 27th, I am having a full-day seminar Advanced Data Modeling Topics in ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on February 24, 2015
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