Data Mining I - Introduction to Data Mining
Information about the inspection of the exam paper is available here.
Timetable
Day | Time | Frequency | Period | Room | Lecturer | Remarks | Max. participants |
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Vorlesung(V) - Lecture - Dates/Times/Location: | |||||||
Tue. | 13:00 bis 15:00 | weekly | G22A-203 (40 Pl.) | Spiliopoulou | 40 | ||
Übung (Ü) - Exercise - Dates/Times/Location: Group 1 | |||||||
Mon. | 09:00 bis 11:00 | weekly | G22A-111 (40 Pl.) | Tutor | 20 | ||
Übung (Ü) - Exercise - Dates/Times/Location: Group 2 | |||||||
Thu. | 15:00 bis 17:00 | weekly | G22A-113 (24 Pl.) | Tutor | 20 |
Overview (from LSF)
Learning Content | 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:
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
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Literature | Pan-Ning Tan, Steinbach, Vipin Kumar. "Introduction to Data Mining", Wiley, 2004 (Auszüge, u.a. aus Kpt. 1-4, 6-8) |
Target Group | English Master DKE English Master DigiEng Export |
Description | Data Mining I - Introduction to Data Mining |
Lecture
Block 1 - Classification - Part 1 (UPDATE 02.05.2017)
Block 1 - Classification - Part 2
Exercise