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An Efficient Approximation Scheme for Data Mining Tasks (2001)
| Citations: | 13 - 0 self |
BibTeX
@MISC{Kollios01anefficient,
author = {George Kollios and Dimitrios Gunopulos and Nick Koudas and Stefan Berchtold},
title = {An Efficient Approximation Scheme for Data Mining Tasks},
year = {2001}
}
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Abstract
We investigate the use of biased sampling according to the density of the dataset, to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multi-dimensional datasets. In density-biased sampling, the probability that a given point will be included in the sample depends on the local density of the dataset. We propose a general technique for density-biased sampling that can factor in user requirements to sample for properties of interest, and can be tuned for specific data mining tasks. This allows great flexibility, and improved accuracy of the results over simple random sampling. We describe our approach in detail, we analytically evaluate it, and show how it can be optimized for approximate clustering and outlier detection. Finally we present a thorough experimental evaluation of the proposed method, applying density-biased sampling on real and synthetic data sets, and employing clustering and outlier detection algorithms, thus highlighti...
Keyphrases
data mining task efficient approximation scheme density-biased sampling large multi-dimensional datasets great flexibility local density general technique user requirement detection algorithm specific data mining task general data mining task synthetic data set thorough experimental evaluation approximate clustering
