Subspace sums for extracting non-random data from massive noise |
| |
Authors: | Anne M Denton |
| |
Affiliation: | (1) Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58108-6050, USA |
| |
Abstract: | An algorithm is introduced that distinguishes relevant data points from randomly distributed noise. The algorithm is related
to subspace clustering based on axis-parallel projections, but considers membership in any projected cluster of a given side
length, as opposed to a particular cluster. An aggregate measure is introduced that is based on the total number of points
that are close to the given point in all possible 2
d
projections of a d-dimensional hypercube. No explicit summation over subspaces is required for evaluating this measure. Attribute values are
normalized based on rank order to avoid making assumptions on the distribution of random data. Effectiveness of the algorithm
is demonstrated through comparison with conventional outlier detection on a real microarray data set as well as on time series
subsequence data.
|
| |
Keywords: | Outlier analysis Noise Gene-expression analysis Density-based clustering Subspace clustering |
本文献已被 SpringerLink 等数据库收录! |
|