Large client-server data intensive applications can place high demands on system and network resources. This is especially true when the connection between the client and server s...
In high dimensional data sets not all dimensions contain an equal amount of information and most of the time global features are more important than local differences. This makes ...
For applications with consecutive incoming training examples, on-line learning has the potential to achieve a likelihood as high as off-line learning without scanning all availabl...
The trend of social information processing sees e-commerce and social web applications increasingly relying on user-generated content, such as rating, to determine the quality of o...
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...