An evolutionary algorithm for mining rare association rules: A Big Data approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{PadilloLV17,
  author =       "Francisco Padillo and Jose Maria Luna and 
                 Sebastian Ventura",
  booktitle =    "Proceedings of the 2017 {IEEE} Congress on
                 Evolutionary Computation",
  title =        "An evolutionary algorithm for mining rare association
                 rules: {A} Big Data approach",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "2007--2014",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Association rule mining is one of the most well known
                 techniques to discover interesting relations between
                 items in data. To date, this task has been mainly
                 focused on the discovery of frequent relationships.
                 However, it is often interesting to focus on those that
                 do not occur frequently. Rare association rule mining
                 is an alluring field aiming at describing rare cases or
                 unexpected behaviour. This field is really useful over
                 Big Data where abnormal endeavour are more curious than
                 common behaviour. In this sense, our aim is to propose
                 a new evolutionary algorithm based on grammars to
                 obtain rare association rules on Big Data. The novelty
                 of our work is that it is eminently designed to be
                 parallel, enabling its use over emerging technologies
                 as Spark and Flink. Furthermore, while other algorithms
                 focus on maximizing a couple of quality measure
                 ignoring the rest, our fitness function has been
                 precisely designed to obtain a trade-off while
                 maximizing a set of well-known quality measures. The
                 experimental study includes more than 70 datasets
                 revealing alluring results in efficiency when more than
                 300 million of instances and file sizes up to 250
                 GBytes are considered, and proving that it is able to
                 run efficiently in huge volumes of data.",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 Big Data, Flink, Spark, association rules mining,
                 evolutionary algorithm, grammars, quality measure,
                 Algorithm design and analysis, Grammar, Proposals,
                 Sparks, Syntactics",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969547",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969547} \cite{padillo:2017:CEC}",
}

Genetic Programming entries for Francisco Padillo Jose Maria Luna Sebastian Ventura

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