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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 ...



