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SIGCSE
2009
ACM
132views Education» more  SIGCSE 2009»
16 years 7 months ago
Retina: helping students and instructors based on observed programming activities
It is difficult for instructors of CS1 and CS2 courses to get accurate answers to such critical questions as "how long are students spending on programming assignments?"...
Christian Murphy, Gail E. Kaiser, Kristin Loveland...
KDD
2009
ACM
153views Data Mining» more  KDD 2009»
16 years 7 months ago
Predicting bounce rates in sponsored search advertisements
This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement: bounce rate. The bounce rate of an ad can be informally defined as t...
D. Sculley, Robert G. Malkin, Sugato Basu, Roberto...
KDD
2007
ACM
137views Data Mining» more  KDD 2007»
16 years 7 months ago
Characterising the difference
Characterising the differences between two databases is an often occurring problem in Data Mining. Detection of change over time is a prime example, comparing databases from two b...
Jilles Vreeken, Matthijs van Leeuwen, Arno Siebes
KDD
2007
ACM
165views Data Mining» more  KDD 2007»
16 years 7 months ago
Stochastic processes and temporal data mining
This article tries to give an answer to a fundamental question in temporal data mining: "Under what conditions a temporal rule extracted from up-to-date temporal data keeps i...
Paul Cotofrei, Kilian Stoffel
KDD
2006
ACM
112views Data Mining» more  KDD 2006»
16 years 7 months ago
K-means clustering versus validation measures: a data distribution perspective
K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation...
Hui Xiong, Junjie Wu, Jian Chen