Data Mining I - Introduction to Data Mining
Appointment to review DM4BA, DM I and Recommenders exams:
17.04.2019: 1000 Uhr 1200hrs R130
***For the students who signed up for a second chance to inspect their examination papers***
Please find the appointment hours here
3rd try for DM I
Wednesday, June 12
Slots between 9:30 and 11:30
Slots between 15:00 and 16:00
Please register via Examinations Office
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 | from 17.04.2018 | G44-H6 (301 Pl.) | Spiliopoulou | ||
Übung (Ü) - Exercise - Dates/Times/Location: Group 1 | |||||||
Mon. | 09:00 bis 11:00 | weekly | G22A-111 (40 Pl.) | Unnikrishnan | 20 | ||
Übung (Ü) - Exercise - Dates/Times/Location: Group 2 | |||||||
Fri. | 11:00 bis 13:00 | weekly | 20.04.2018 to 06.07.2018 | G05-210 (40 Pl.) | Tutor | ||
Übung (Ü) - Exercise - Dates/Times/Location: Group 3 | |||||||
Mon. | 15:00 bis 17:00 | weekly | G22A-210 (24 Pl.) | Tutor | 20 | ||
Übung (Ü) - Exercise - Dates/Times/Location: Group 4 | |||||||
Tue. | 11:00 bis 13:00 | weekly | G22A-210 (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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Description |
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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) |
Remarks | Attention, new room! The event "Data Mining I - Introduction to Data Mining" takes place in G44-H6. |
Target Group | English Master DKE English Master DigiEng Export |
Description | Data Mining I - Introduction to Data Mining |
Lecture
Introduction and Administratives
Block "Classification" (with modifications)
Block "Frequent Itemset Discovery for Association Rule Learning and Classification Rule Learning"
[Paper] X-means: Extending k-means with efficient estimation of the number of clusters.
Exercise