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  • Embrace R @ SQL Nexus 2017 & SQL Saturday #626

    R is the hottest topic in SQL Server 2016. If you want to learn how to use it for advanced analytics, join my seminar at SQL Nexus conference on my 1st in Copenhagen. Although there is still nearly a month before the seminar, there are less than half places still available. You are also very welcome to visit my session Using R in SQL Server, Power ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on April 2, 2017
  • Data Mining Algorithms – Pluralsight Course

    This is a bit different post in the series about the data mining and machine learning algorithms. This time I am honored and humbled to announce that my fourth Pluralsight course is alive. This is the Data Mining Algorithms in SSAS, Excel, and R course. besides explaining the algorithms, I also show demos in different products. This gives you even ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on July 30, 2015
  • 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
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