Can frequent itemset mining be efficiently and effectively used for learning from graph data?
2012 (English)In: 11th International Conference on Machine Learning and Applications (ICMLA) / [ed] Juan E. Guerrero, IEEE Computer Society, 2012, 409-414 p.Conference paper (Refereed)
Standard graph learning approaches are often challenged by the computational cost involved when learning from very large sets of graph data. One approach to overcome this problem is to transform the graphs into less complex structures that can be more efficiently handled. One obvious potential drawback of this approach is that it may degrade predictive performance due to loss of information caused by the transformations. An investigation of the tradeoff between efficiency and effectiveness of graph learning methods is presented, in which state-of-the-art graph mining approaches are compared to representing graphs by itemsets, using frequent itemset mining to discover features to use in prediction models. An empirical evaluation on 18 medicinal chemistry datasets is presented, showing that employing frequent itemset mining results in significant speedups, without sacrificing predictive performance for both classification and regression.
Place, publisher, year, edition, pages
IEEE Computer Society, 2012. 409-414 p.
Graph learning, frequent itemset mining, classification, regression
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-86335DOI: 10.1109/ICMLA.2012.74ISBN: 978-0-7695-4913-2OAI: oai:DiVA.org:su-86335DiVA: diva2:586639
ICMLA 2012, December 12-15, Boca Raton, Florida, USA