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SSD
2005
Springer
173views Database» more  SSD 2005»
14 years 1 months ago
On Discovering Moving Clusters in Spatio-temporal Data
A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars movin...
Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras
CIKM
2009
Springer
13 years 11 months ago
Efficient itemset generator discovery over a stream sliding window
Mining generator patterns has raised great research interest in recent years. The main purpose of mining itemset generators is that they can form equivalence classes together with...
Chuancong Gao, Jianyong Wang
KDD
1994
ACM
96views Data Mining» more  KDD 1994»
13 years 12 months ago
DICE: A Discovery Environment Integrating Inductive Bias
: Most of Knowledge Discovery in Database (KDD) systems are integrating efficient Machine Learning techniques. In fact issues in Machine Learning and KDD are very close allowing fo...
Jean-Daniel Zucker, Vincent Corruble, J. Thomas, G...
KDD
2005
ACM
106views Data Mining» more  KDD 2005»
14 years 1 months ago
Enhancing the lift under budget constraints: an application in the mutual fund industry
A lift curve, with the true positive rate on the y-axis and the customer pull (or contact) rate on the x-axis, is often used to depict the model performance in many data mining ap...
Lian Yan, Michael Fassino, Patrick Baldasare
ICDE
2009
IEEE
176views Database» more  ICDE 2009»
14 years 9 months ago
Discovering Conditional Functional Dependencies
This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantic...
Wenfei Fan, Floris Geerts, Laks V. S. Lakshmanan, ...