Mining Interesting Temporal Rules with Genetic Programming and Specialized Hardware

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{DBLP:conf/icmla/SaetromH03,
  author =       "Pal Saetrom and Magnus Lie Hetland",
  title =        "Mining Interesting Temporal Rules with Genetic
                 Programming and Specialized Hardware",
  year =         "2003",
  pages =        "145--151",
  editor =       "M. Arif Wani and Krzysztof J. Cios and Khalid Hafeez",
  publisher =    "CSREA Press",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  booktitle =    "Proceedings of the 2003 International Conference on
                 Machine Learning and Applications - ICMLA 2003",
  address =      "Los Angeles, California, USA",
  month =        jun # " 23-24",
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 rule discovery, time series, pattern matching
                 hardware",
  URL =          "http://hetland.org/research/2003/icmla2003.pdf",
  abstract =     "Rule mining is the practice of discovering interesting
                 and unexpected rules from large data sets. Depending on
                 the exact problem formulation, this may be a very
                 complicated problem. Existing methods typically make
                 strong simplifying assumptions about the form of the
                 rules, and limit the measure of rule quality to simple
                 properties, such as confidence. Because confidence in
                 itself is not a good indicator of how interesting a
                 rule is to the user, the mined rules are typically
                 sorted according to some secondary interestingness
                 measure. In this paper we present a rule mining method
                 that is based on genetic programming. Because we use
                 specialized pattern matching hardware to evaluate each
                 rule, our method supports a very wide range of rule
                 formats, and can use any reasonable fitness measure. We
                 develop a fitness measure that is well-suited for our
                 method, and give empirical results of applying the
                 method to synthetic and real-world data sets.",
  notes =        "Hetland's web site (2009) gives title as
                 {"}Unsupervised Temporal Rule Mining with Genetic
                 Programming and Specialized Hardwared{"}

                 'This pattern matching chip (PMC), is able to search
                 100 MB/s and can handle from 1 to 64 parallel queries,
                 depending on query complexity.'",
}

Genetic Programming entries for Pal Saetrom Magnus Lie Hetland

Citations