Data mining is data analysis + algorithmics.
The objective is to develop methods to efficiently extract
useful information from large data sets.
I have made contributions to the analytical foundations
of this field, as well as to applications within
personalized information systems.
Please note that I take great care to safeguard any private
personal information that is needed in my work.
My work in data mining with colleagues and students at WPI
has focused on the paradigm of association rules.
I am also collaborating with medical colleagues from the
U. of Massachusetts Medical School.
My most recent work involves the discovery of statistically
significant patterns in data arising in
human sleep studies
Selected papers on data mining (asterisks * denote student co-authors)
A. Khasawneh*, S. A. Alvarez, C. Ruiz, S. Misra*, and M. Moonis.
"Discovery of Sleep Composition Types using Expectation-Maximization",
Proc. 23rd IEEE International Symposium on Computer-Based Medical Systems
(CBMS 2010), Perth, Australia, Oct. 12-15, 2010, to appear
J. Hayward*, S. A. Alvarez, C. Ruiz, M. Sullivan, J. Tseng, and G. Whalen.
"Machine Learning of Clinical Performance in a Pancreatic Cancer Database",
Artificial Intelligence in Medicine, special issue on Data Mining
Approaches to the Study of Disease Genes and Proteins (Sun Kim, ed.),
vol. 49, issue 3, July 2010, 187-195
P. Laxminarayan*, S.A. Alvarez, C. Ruiz, and M. Moonis.
"Mining Statistically Significant Associations for Exploratory
Analysis of Human Sleep Data",
IEEE Transactions on Information Technology in Biomedicine,
Vol. 10, No. 3, 440-450, July 2006
"Chi-Squared Computation for Association Rules: Preliminary Results",
Technical Report BC-CS-2003-01, Computer Science Department,
Boston College, July 2003
- Lin*, W.-Y., Alvarez, S. A., Ruiz, C.,
"Efficient Adaptive-Support Association Rule Mining for Recommender Systems",
Data Mining and Knowledge Discovery,
Vol. 6, No. 1, pp. 83-105, Jan. 2002