Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets (1998)
Tags
AD-trees, Association Rules, Bayesian Networks, Cached Sufficient Statistics, Efficient Statistical Algorithms
Abstract
This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its...
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Approximate BibTeX Entry
@article{moore-cached,
Month = {March},
Year = {1998},
Journal = {Journal of Artificial Intelligence Research},
Volume = {8},
Pages = {67-91},
Author = {
Andrew Moore, Mary Soon
Lee
},
Title = {Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets}
}