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Can frequent itemset mining be efficiently and effectively used for learning from graph data?
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2012 (engelsk)Inngår i: 11th International Conference on Machine Learning and Applications (ICMLA) / [ed] Juan E. Guerrero, IEEE Computer Society, 2012, s. 409-414Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2012. s. 409-414
Emneord [en]
Graph learning, frequent itemset mining, classification, regression
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-86335DOI: 10.1109/ICMLA.2012.74ISBN: 978-0-7695-4913-2 (tryckt)OAI: oai:DiVA.org:su-86335DiVA, id: diva2:586639
Konferanse
ICMLA 2012, December 12-15, Boca Raton, Florida, USA
Tilgjengelig fra: 2013-01-12 Laget: 2013-01-12 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Inngår i avhandling
1. Learning predictive models from graph data using pattern mining
Åpne denne publikasjonen i ny fane eller vindu >>Learning predictive models from graph data using pattern mining
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimization problem with two objectives, efficiency and effectiveness, where improving one objective without the other objective being worse off is a better solution, called a Pareto improvement. In this thesis, it is investigated how to improve the efficiency and effectiveness of learning from graph data using pattern mining methods. Two objectives are set where one concerns how to improve the efficiency of pattern mining without reducing the predictive performance of the learning models, and the other objective concerns how to improve predictive performance without increasing the complexity of pattern mining. The employed research method mainly follows a design science approach, including the development and evaluation of artifacts. The contributions of this thesis include a data representation language that can be characterized as a form in between sequences and itemsets, where the graph information is embedded within items. Several studies, each of which look for Pareto improvements in efficiency and effectiveness are conducted using sets of small graphs. Summarizing the findings, some of the proposed methods, namely maximal frequent itemset mining and constraint based itemset mining, result in a dramatically increased efficiency of learning, without decreasing the predictive performance of the resulting models. It is also shown that additional background knowledge can be used to enhance the performance of the predictive models, without increasing the complexity of the graphs.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. s. 118
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-003
Emneord
Machine Learning, Graph Data, Pattern Mining, Classification, Regression, Predictive Models
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-100713 (URN)978-91-7447-837-2 (ISBN)
Disputas
2014-03-25, room B, Forum, Isafjordsgatan 39, Kista, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-03-03 Laget: 2014-02-11 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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