Data mining

Data mining involves developing techniques rooted in statistics and machine learning to efficiently extract meaningful and useful information from large collections of data. I have made contributions to the analytical foundations of this field, as well as to applications within medicine and personalized information systems. Please note that I take great care to safeguard any private personal information that is needed in my work.

Much of my recent work involves the modeling of data arising in human sleep studies, in collaboration with colleagues and students at WPI. I have also collaborated with medical colleagues from the U. of Massachusetts Medical School. We've developed a variety of approaches, from probabilistic clustering to deep learning. We've also applied data mining techniques in surgical oncology.

My earliest work in data mining focused on the paradigm of association rules (e.g., 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).

Selected papers on data mining (asterisks * denote student co-authors)