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@conference{,
  abstract = {An episode rule of associating two episodes represents a temporal implication of the antecedent episode to the consequent episode. Episode-rule mining is a task of extracting useful patterns/episodes from large event databases. We present an episode-rule mining algorithm for finding frequent and confident serial-episode rules via first local-maximum confidence in yielding ideal window widths, if exist, in event sequences based on minimal occurrences constrained by a constant maximum gap. Results from our preliminary empirical study confirm the applicability of the episode-rule mining algorithm for Web-site traversal-pattern discovery, and show that the first local maximization yielding ideal window widths exists in real data but rarely in synthetic random data sets.},
  address = {New York, NY, USA},
  author = {Dai, H. K.},
  booktitle = {Proceedings of the 9th International Symposium on Information and Communication Technology},
  doi = {10.1145/3287921.3287982},
  isbn = {9781450365390},
  keywords = {Web-site traversal pattern, episode-rule mining, first local maximization},
  month = {12},
  pages = {130--136},
  publisher = {Association for Computing Machinery},
  series = {SoICT '18},
  title = {Episode-Rule Mining with Minimal Occurrences via First Local Maximization in Confidence},
  year = {2018},
}

@book{,
  editor = {Bhattacharyya, R. and McCormick, M. E.},
  file = {/home/mfg/acad/ext/arts/waves/water/[R._Bhattacharyya_and_M.E._McCormick_(Eds.)]_Wave_(z-lib.org).pdf},
  isbn = {9780080442129},
  keywords = {waves, agua},
  publisher = {Elsevier Science},
  title = {Wave Energy Conversion},
  year = {2013},
}

@Comment{jabref-meta: databaseType:bibtex;}

