In this seminar, the students will learn data mining techniques for discovering frequent itemsets. The discovery of frequent itemsets is a very useful technique for analyzing data, generating association rules, deriving machine learning features and many other applications.
We expect the students to examine existing techniques by implementing (and possibly improving) one approach and an extension of that approach (for instance "multisets" or "utility patterns"). You should develop a suitable use case for that extension of the chosen frequent pattern analysis algorithm. The students are free to use any data set for their use case. We regard the DBpedia Infobox triples as a promising option. At the end of the seminar, the students are asked to evaluate their implemented algorithm on their use case both quantitatively and qualitatively.
The maximum number of students is 6, resulting in 3 teams.