Publications

@inproceedings{augelloSensingWebInduction2020,

title = {Sensing the Web for Induction of Association Rules and Their Composition through Ensemble Techniques},

author = { Agnese Augello and Ignazio Infantino and Giovanni Pilato and Filippo Vella},

editor = { Klimov V.V. Samsonovich A.V.},

doi = {10.1016/j.procs.2020.02.152},

year  = {2020},

date = {2020-01-01},

booktitle = {Procedia Computer Science},

volume = {169},

pages = {851--859},

publisher = {Elsevier B.V.},

abstract = {Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules. textcopyright 2020 The Authors. Published by Elsevier B.V.},

keywords = {Association Rules, Semantic Computing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augello_sensing_2020,

title = {Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques},

author = {Agnese Augello and Ignazio Infantino and Giovanni Pilato and Filippo Vella},

editor = {Klimov V. V. Samsonovich A.V.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084511526&doi=10.1016%2fj.procs.2020.02.152&partnerID=40&md5=12d19357a00b9c71e978abdae0e82bc9},

doi = {10.1016/j.procs.2020.02.152},

year  = {2020},

date = {2020-01-01},

booktitle = {Procedia Computer Science},

volume = {169},

pages = {851–859},

publisher = {Elsevier B.V.},

abstract = {Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules. © 2020 The Authors. Published by Elsevier B.V.},

keywords = {Association Rules, Semantic Computing},

pubstate = {published},

tppubtype = {inproceedings}

}