@inproceedings{017666988df846e68cafdde439d5d250,
title = "Investigation of rule interestingness in medical data mining",
abstract = "This research experimentally investigates the performance of conventional rule interestingness measures and discusses their usefulness for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected sixteen kinds of interestingness measures for the rules discovered in a dataset on hepatitis. χ2 measure, recall, and accuracy demonstrated the highest performance, and specificity and prevalence did the lowest. The interestingness measures showed a complementary relationship for each other. These results indicated that some interestingness measures have the possibility to predict really interesting rules at a certain level and that the combinational use of interestingness measures will be useful. We then discussed how to combinationally utilize interestingness measures and proposed a post-processing user interface utilizing them, which supports KDD through human-system interaction.",
author = "Miho Ohsaki and Shinya Kitaguchi and Hideto Yokoi and Takahira Yamaguchi",
year = "2005",
doi = "10.1007/11423270_10",
language = "English",
isbn = "3540261575",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "174--189",
booktitle = "Active Mining - Second International Workshop, AM 2003, Revised Selected Papers",
note = "Second International Workshop on Active Mining, AM 2003 ; Conference date: 28-10-2003 Through 31-10-2003",
}
