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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2016

01.04.2016 | Regular Paper

Efficient discovery of contrast subspaces for object explanation and characterization

verfasst von: Lei Duan, Guanting Tang, Jian Pei, James Bailey, Guozhu Dong, Vinh Nguyen, Akiko Campbell, Changjie Tang

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2016

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Abstract

We tackle the novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes \(C_+\) and \(C_-\) and a query object \(o\), we want to find the top-\(k\) subspaces that maximize the ratio of likelihood of \(o\) in \(C_+\) against that in \(C_-\). Such subspaces are very useful for characterizing an object and explaining how it differs between two classes. We demonstrate that this problem has important applications, and, at the same time, is very challenging, being MAX SNP-hard. We present CSMiner, a mining method that uses kernel density estimation in conjunction with various pruning techniques. We experimentally investigate the performance of CSMiner on a range of data sets, evaluating its efficiency, effectiveness, and stability and demonstrating it is substantially faster than a baseline method.
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Fußnoten
1
While [8] presented a contrast-pattern length based algorithm to detection global outliers, their problem setting is different from ours.
 
2
Generally, given a set of observations \(Q\), the plausibility of two models \(M_1\) and \(M_2\) can be assessed by the Bayes factor \(K=\frac{Pr(Q\mid M_1)}{Pr(Q \mid M_2)}\).
 
3
If it is not unimodal, then there could be multiple peaks at different distances from the query, which is counter to intuition. Similarly, we have no basis for preferring any direction over another, so symmetry is natural.
 
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Metadaten
Titel
Efficient discovery of contrast subspaces for object explanation and characterization
verfasst von
Lei Duan
Guanting Tang
Jian Pei
James Bailey
Guozhu Dong
Vinh Nguyen
Akiko Campbell
Changjie Tang
Publikationsdatum
01.04.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2016
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-015-0835-6

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