Nathan Rountree

Nathan Rountree is a lecturer in computer science at the University of Otago (Dunedin, New Zealand), where he teaches papers on databases, data structures and algorithms, and Web development. He holds a bachelor's degree in music, a postgraduate diploma in computer science, and a PhD in computer science, all from Otago. His research interests include computer science education, artificial neural networks, and collaborative filtering.

Publications

Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection
Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection
Yun Sing Koh, Nathan Rountree. © 2010. 320 pages.
The growing complexity and volume of modern databases make it increasingly important for researchers and practitioners involved with association rule mining to make sense of the...
Rare Association Rule Mining: An Overview
Rare Association Rule Mining: An Overview
Yun Sing Koh, Nathan Rountree. © 2010. 14 pages.
The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule...
He Wasn't There Again Today
He Wasn't There Again Today
Richard A. O’Keefe, Nathan Rountree. © 2010. 11 pages.
In this chapter, the authors discuss the characteristics of data collected by the New Zealand Centre for Adverse Drug Reaction Monitoring (CARM) over a five-year period. The...
Interestingness Measures for Association Rules: What Do They Really Measure?
Interestingness Measures for Association Rules: What Do They Really Measure?
Yun Sing Koh, Richard O’Keefe, Nathan Rountree. © 2008. 23 pages.
Association rules are patterns that offer useful information on dependencies that exist between the sets of items. Current association rule mining techniques such as apriori...
Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse
Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse
Yun Sing Koh, Nathan Rountree, Richard O’Keefe. © 2008. 13 pages.
Discovering association rules efficiently is an important data mining problem. We define sporadic rules as those with low support but high confidence; for example, a rare...
Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse
Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse
Yun Sing Koh, Nathan Rountree, Richard O’Keefe. © 2006. 17 pages.
Discovering association rules efficiently is an important data mining problem. We define sporadic rules as those with low support but high confidence; for example, a rare...