Ranking Interesting Subspaces for Clustering High Dimensional Data
Rating:
(0)
(0)
Overview: The tremendous amount of data produced nowadays in various application domains such as molecular biology can only be fully exploited by efficient and effective data mining tools. One of the primary data mining tasks is clustering which is the task of partitioning objects of a data set into distinct groups (clusters) such that two objects from one cluster are similar to each other, whereas two objects from distinct clusters are not. Considerable work has been done in the area of clustering. Nevertheless, clustering real-world data sets often raises problems, since the data space is usually a high dimensional feature space.
Not what you’re looking for? Search again
More From the Data Mining Section
More Development Resources
Upcoming Webcasts
More On Development
|
