Ruprecht-Karls-Universität Heidelberg
Siegel der Universität Heidelberg

Module for [Scientific Computing]

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[Knowledge Discovery in Databases] - [2015 Sommer]

Module Code
IKDD
Name
Knowledge Discovery in Databases
Credit Points
8 LP CP
Workload
240 h
Duration
ein Semester
Cycle
0
Methods Lecture 4 h + Exercise course 2 h
Objectives
Content The KDD process Basics: Data, statistics, and probability theory Data preprocessing: data quality, duplicate detection, data integration Data clustering: k-means, hierarchical clustering, densitybased clustering, cluster evaluation Frequent pattern mining: frequent itemsets, mining sequential data, mining with constraints Classification: decision trees, bayes classifier, overfitting, support vector machines Outlier detection: statistical approaches, clustering-based techniques, density-based techniques Graph mining: subgraph patterns, graph clustering, indexing Mining spatial and spatio-temporal data
Learning outcomes Knowing the requirements and methods underlying the different steps of the KDD process Knowing the concepts and techniques related to different clustering approaches Knowing the concepts and techniques related to different classification approaches Knowing the concepts and techniques related to different frequent pattern mining approaches Be able to apply different data preprocessing and mining techniques for analyzing and exploring data Be familiar with the support of data mining approaches using relational database management system Knowing fundamental methods underlying graph mining
Prerequisitesnone
Suggested previous knowledge Algorithms and data structures (IAD); Introduction to databases (IDB1)
Assessments Assignments; at least 50% of the credit points for the assignments need to be obtained to be eligible to participate in the final written exam
Literature Jiawei Han und Micheline Kamber: Data Mining. Concepts and Techniques, Morgan Kaufmann Series in Data Management Systems (2nd Edition), 2006.
Martin Ester und Jörg Sander: Knowledge Discovery in Databases: Techniken und Anwendungen, Springer, 2000.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining. Addison Wesley, 2005.
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