Data mining is a family of methods used e.g. in recommenders and in decision support systems for prediction, for customer profiling, for classification and outlier detection. For example:

• A decision maker decides which products should be offered to Internet-customers.
• The decision maker decides, when a product will be recommended to a customer, whether the customer obtains ads and how these ads look like.
• The decision maker may be a human or an intelligent service (as embedded in a recommendation engine)

For such decisions, the decision maker uses models that captures the preferences, price sensitivity and attitudes of customers, the behaviour of customers and the similarity among customers. In this bachelor course, we discuss methods for deriving models from data. In particular, we discuss

• Classification (example applications: spam recognition, distinguishing between malignant and benign tumors)
• Clustering (example applications: customer profile learning, outlier detection)
• Association rules (example applications: market basket analysis for cross selling and up selling, recommenders)

Pan-Ning Tan, Steinbach, Vipin Kumar. "Introduction to Data Mining", Wiley, 2004 (Auszüge, u.a. aus Kpt. 1-4, 6-8)
Selection of scientific. papers, announced during the start of the lecture

Attention, new room! The event "Data Mining I - Introduction to Data Mining" takes place in G44-H6.

English Master DKE

English Master DigiEng

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