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So we are back. PASS SQL Saturday is coming to Slovenia again on December 12th, 2015. Remember last two years? We had two great events. According to feedback, everybody was satisfied and happy. Let's make another outstanding event! How can you help?
First of all, these events are free for attendees. Of course, this is possible only because ...

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 ...

Principal component analysis (PCA) is a technique used to emphasize the majority of the variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. It is closely connected to eigenvectors and eigenvalues.
A short definition of the algorithm: PCA uses an orthogonal transformation to convert ...


With the KMeans 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 ...

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 ...

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 KMeans and ExpectationMaximization ...

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 ...

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 ...

The Association Rules algorithm is specifically designed for use in market basket analyses. This knowledge can additionally help in identifying crossselling 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 ...
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