Download Xavier University Chemistry Department Research Report

Abstract Information for
Xavier Celebration of Student Research and Creative Activity
April 4, 2005
Abstract Instructions
Please indicate type of presentation:
___ poster
_X_ 15 min. oral presentation
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The abstract title should be typed in capital letters. Only the title line should be in capital
letters. Immediately following the title line should be a line that contains the Authors' Names
(first name, middle initial, last name.) The faculty mentor should be the last author listed and
this name should be placed in parenthesis. The next line should be the department name. The
abstract should follow starting on the next line and there should be no indentation. All
information must fit into the space provided. No changes are to be made to the font size or
margins of this page to accommodate your abstract.
E-mail an attached file copy of this form with the appropriate changes to the content of the
example abstract below to Dr. Barbara Hopkins at [email protected]. Also print a final copy
of the form and mail this paper copy to Mary Alice Vetter, ML 4543.
Final day for submission of abstracts is March 7. See second attachment for examples.
Use only the area below to type your abstract. Change content but do not change font or
margins.
MINING DISJUNCTIVE ASSOCIATION RULES USING GENETIC PROGRAMMING
Michelle A. Lyman (Dr. Gary Lewandowski)
Department of Mathematics and Computer Science
Machine learning techniques can be used to mine trends in data sets. This paper reports findings mined from data
collected in a card sorting exercise performed by beginning Computer Science students and their professors.
Participants sorted 26 cards with Computer Science-related concepts written on them into groups based on their
understanding of the concepts. An application was developed to mine overarching trends in the way the
participants sorted the cards. These trends were expressed in the form of disjunctive association rules. The
application randomly generated association rules and then used genetic programming techniques to evolve them
into rules with high support and confidence. When the card sorting data was segmented according to the
achievement level of the participant, differences in the number of association rules produced by each group could
be observed. This information, along with the actual association rules, can be used to present Computer Science
concepts more effectively to beginning Computer Science students.