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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of ...



