Guozhu Dong (Advisor), Yong Pei (Committee Member), Thomas Sudkamp (Other), Krishnaprasad Thirunarayan (Committee Member), Joseph F. Thomas, Jr. (Other)
Master of Science (MS)
Given a pair of objects, it is of interest to know how they are related to each other and the strength of their similarity. Many previous studies focused on two types of similarity measures: The first type is based on closeness of attribute values of two given objects, and the second type is based on how often the two objects co-occur in transactions/tuples.
In this thesis we study a new behavior-based similarity measure, which evaluates similarity between two objects by considering how similar their correlated third-party object sets are. Behavior-based similarity can help us find pairs of objects that have similar external functions but do not have very similar attribute values or do not co-occur quite often.
After introducing and formalizing behavior-based similarity, we give an algorithm to mine pairs of similar objects under this measure. We demonstrate the usefulness of our algorithm and this measure using experiments on several news and medical datasets.
Department or Program
Department of Computer Science
Year Degree Awarded
Copyright 2008, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.