Knowledge and Data Management White Papers
Ranking Interesting Subspaces for Clustering High Dimensional Data
Overview Application domains such as life sciences, e.g. molecular biology produce a tremendous amount of data which can no longer be managed without the help of efficient and effective data mining methods. One of the primary data mining tasks is clustering. However, traditional clustering algorithms often fail to detect meaningful clusters because of the high dimensional, inherently sparse feature space of most real-world data sets. Nevertheless, the data sets often contain clusters hidden in various subspaces of the original feature space. The paper presents a pre-processing step for traditional clustering algorithms, which detects all interesting subspaces of high-dimensional data containing clusters.
| Publisher | Springer Science+Business Media | File Format | |
|---|---|---|---|
| Date Published | January 2007 | ||
| Format | White Papers | ||
| Topics | |||
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