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EUROPAR
1999
Springer
15 years 11 months ago
Parallel k/h-Means Clustering for Large Data Sets
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We ...
Kilian Stoffel, Abdelkader Belkoniene
AAIM
2006
Springer
143views Algorithms» more  AAIM 2006»
16 years 17 days ago
A Compression-Boosting Transform for Two-Dimensional Data
We introduce a novel invertible transform for two-dimensional data which has the objective of reordering the matrix so it will improve its (lossless) compression at later stages. T...
Qiaofeng Yang, Stefano Lonardi, Avraham Melkman
DEXA
2008
Springer
130views Database» more  DEXA 2008»
15 years 8 months ago
Classifying Evolving Data Streams Using Dynamic Streaming Random Forests
We consider the problem of data-stream classification, introducing a stream-classification algorithm, Dynamic Streaming Random Forests, that is able to handle evolving data streams...
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma...
FLAIRS
2004
15 years 8 months ago
Blind Data Classification Using Hyper-Dimensional Convex Polytopes
A blind classification algorithm is presented that uses hyperdimensional geometric algorithms to locate a hypothesis, in the form of a convex polytope or hyper-sphere. The convex ...
Brent T. McBride, Gilbert L. Peterson
BMVC
1998
15 years 8 months ago
A Method for Dynamic Clustering of Data
This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Ko...
Arnaldo J. Abrantes, Jorge S. Marques