Cited By
View all- Hassinen MUkkonen A(2018)A Progressive Resampling Algorithm for Finding Very Sparse Investment PortfoliosECML PKDD 2018 Workshops10.1007/978-3-030-13463-1_5(66-80)Online publication date: 10-Sep-2018
Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling
Authors: 
Sandy Moens, Mario BoleyAuthors Info & Claims
Sandy Moens, Mario BoleyAuthors Info & ClaimsPages 203 - 214
Abstract
When plugged into instant interactive data analytics processes, pattern mining algorithms are required to produce small collections of high quality patterns in short amounts of time. In the case of Exceptional Model Mining (EMM), even heuristic approaches like beam search can fail to deliver this requirement, because in EMM each search step requires a relatively expensive model induction. In this work, we extend previous work on high performance controlled pattern sampling by introducing extra weighting functionality, to give more importance to certain data records in a dataset. We use the extended framework to quickly obtain patterns that are likely to show highly deviating models. Additionally, we combine this randomized approach with a heuristic pruning procedure that optimizes the pattern quality further. Experiments show that in contrast to traditional beam search, this combined method is able to find higher quality patterns using short time budgets.
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- Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling
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Published In
Oct 2014
45 pages
ISBN:978-3-319-12570-1
DOI:10.1007/978-3-319-12571-8
© Springer International Publishing Switzerland 2014.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 07 March 2023
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View all- Hassinen MUkkonen A(2018)A Progressive Resampling Algorithm for Finding Very Sparse Investment PortfoliosECML PKDD 2018 Workshops10.1007/978-3-030-13463-1_5(66-80)Online publication date: 10-Sep-2018