Title:
A Clustering Algorithm to Discover Low and High Density Hyper-Rectangles in Subspaces of Multidimensional Data.
A Clustering Algorithm to Discover Low and High Density Hyper-Rectangles in Subspaces of Multidimensional Data.
Author(s)
Omiecinski, Edward
Navathe, Shamkant B.
Ezquerra, Norberto F.
Ordońẽz, Carlos
Navathe, Shamkant B.
Ezquerra, Norberto F.
Ordońẽz, Carlos
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Abstract
This paper presents a clustering algorithm to discover low and high density
regions in subspaces of multidimensional data for Data Mining applications.
High density regions generally refer to typical cases, whereas low density
regions indicate infrequent and thus rare cases. For typical applications
there is a large number of low density regions and a few of these are
interesting. Regions are considered interesting when they have a minimum
"volume" and involve some maximum number of dimensions. Our algorithm
discovers high density regions (clusters) and low density regions (outliers,
negative clusters, holes, empty regions) at the same time. In particular,
our algorithm can find empty regions; that is, regions having no data
points. The proposed algorithm is fast and simple. There is a large variety
of applications in medicine, marketing, astronomy, finance, etc, where
interesting and exceptional cases correspond to the low and high density
regions discovered by our algorithm.
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Date Issued
1999
Extent
169683 bytes
Resource Type
Text
Resource Subtype
Technical Report