Univerza na Primorskem Fakulteta za matematiko, naravoslovje in informacijske tehnologije

nedelja, 24. maj 2020 Online seminar: Clustering class association rules to form a Compact and Meaningful Associative Classifier

V ponedeljek, 25. maja 2020, bo ob 16.00 uri prek spletnih orodij na daljavo izvedeno predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 25. maj 2020 ob 16.00 na daljavo 


Jamolbek Mattiev has a Master degree in Computer Science from National University of Uzbekistan. He was awarded with first degree diploma at “the best Master Dissertation Work of Uzbekistan” competition in his master studies. He is a Teaching Assistant at the University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies and he is the third year PhD student at the Department of Information Sciences and Technologies.
He is doing his PhD in the field of Computer Science (Data Mining). His research fields include Artificial Intelligence, Data Mining, Machine Learning. In particular, the sub-fields of Supervised and Unsupervised Learning, Frequent Pattern Discovery, Association Rule Learning, Classification and Clustering.

NASLOV: Clustering class association rules to form a Compact and Meaningful Associative Classifier
Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rules discovered on those big datasets can easily exceed thousands of rules. To produce compact and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. To solve this problem researchers have proposed several associative classification approaches that combine two important data mining techniques, namely, classification and association rule mining.

In this research, we propose a new method that is able to reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier – CMAC, that uses agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules.


Predavanje bo potekalo v angleškem jeziku prek spletnega orodja Zoom.
Do predavanja dostopate tako, da se povežete prek sledeče povezave: