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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
15 years 10 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
DATAMINE
2006
127views more  DATAMINE 2006»
14 years 9 months ago
Computing LTS Regression for Large Data Sets
Least trimmed squares (LTS) regression is based on the subset of h cases (out of n) whose least squares t possesses the smallest sum of squared residuals. The coverage h may be se...
Peter Rousseeuw, Katrien van Driessen
PVLDB
2008
107views more  PVLDB 2008»
14 years 9 months ago
Finding relevant patterns in bursty sequences
Sequence data is ubiquitous and finding frequent sequences in a large database is one of the most common problems when analyzing sequence data. Unfortunately many sources of seque...
Alexander Lachmann, Mirek Riedewald
KDD
2002
ACM
155views Data Mining» more  KDD 2002»
15 years 10 months ago
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Hichem Frigui
ICEIS
2009
IEEE
15 years 4 months ago
Minable Data Warehouse
Data warehouses have been widely used in various capacities such as large corporations or public institutions. These systems contain large and rich datasets that are often used by ...
David Morgan, Jai W. Kang, James M. Kang