Don’t Be Afraid of Simpler Patterns

Bringmann, Björn; Zimmermann, Albrecht; Raedt, Luc De; Nijssen, Siegfried

doi:10.1007/11871637_10

Cited by 48 publications

(86 citation statements)

References 13 publications

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“…The first approach generates frequent outerplanar subgraphs based on the BBP subgraph isomorphism using the mining algorithm FOG presented in [8]. The second approach generates frequent subgraphs based on the general subgraph isomorphism, using an efficient implementation [21] of the gSpan algorithm [12]. In both cases, the bit-vector encodes the occurrence of the frequent patterns.…”

Section: Methodsmentioning

confidence: 99%

An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules

et al. 2008

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Abstract. In machine learning, there has been an increased interest in metrics on structured data. The application we focus on is drug discovery. Although graphs have become very popular for the representation of molecules, a lot of operations on graphs are NP-complete. Representing the molecules as outerplanar graphs, a subclass within general graphs, and using the block-and-bridge preserving subgraph isomorphism, we define a metric and we present an algorithm for computing it in polynomial time. We evaluate this metric and more generally also the blockand-bridge preserving matching operator on a large dataset of molecules, obtaining favorable results.

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Section: Methodsmentioning

confidence: 99%

An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules

et al. 2008

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“…a) E-mail: saigo@bio.kyutech.ac.jp DOI: 10.1587/transinf.E96.D.1766 More advanced approaches attempt to mine discriminative graphs by using the labels of examples as external information source to prune the search space [8]. Correlation or Information gain are typically employed to estimate the informativeness of patterns and prune uninteresting patterns.…”

Section: Introductionmentioning

confidence: 99%

Fast Iterative Mining Using Sparsity-Inducing Loss Functions

Saigo

Kashima

Tsuda³

2013

IEICE Trans. Inf. & Syst.

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SUMMARYApriori-based mining algorithms enumerate frequent patterns efficiently, but the resulting large number of patterns makes it difficult to directly apply subsequent learning tasks. Recently, efficient iterative methods are proposed for mining discriminative patterns for classification and regression. These methods iteratively execute discriminative pattern mining algorithm and update example weights to emphasize on examples which received large errors in the previous iteration. In this paper, we study a family of loss functions that induces sparsity on example weights. Most of the resulting example weights become zeros, so we can eliminate those examples from discriminative pattern mining, leading to a significant decrease in search space and time. In computational experiments we compare and evaluate various loss functions in terms of the amount of sparsity induced and resulting speed-up obtained.

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“…Graph mining systems then perform a complete search through the entire graph space, enumerating all subgraphs satisfying these constraints (Yan and Han 2002;Bringmann et al 2006) or even exhaustively enumerating all possible subgraphs (Wale et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…While these approaches offer strong guarantees w.r.t. completeness or optimality of the found patterns, they have a high computational cost and require post-processing to deal, for example, with redundancy issues (Bringmann et al 2006). In this way, local pattern mining acts as a complex, expensive and indirect approach to generate features for graphs.…”

Section: Introductionmentioning

confidence: 99%

Effective feature construction by maximum common subgraph sampling

et al. 2010

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The standard approach to feature construction and predictive learning in molecular datasets is to employ computationally expensive graph mining techniques and to bias the feature search exploration using frequency or correlation measures. These features are then typically employed in predictive models that can be constructed using, for example, SVMs or decision trees. We take a different approach: rather than mining for all optimal local patterns, we extract features from the set of pairwise maximum common subgraphs. The maximum common subgraphs are computed under the block-and-bridge-preserving subgraph isomorphism from the outerplanar examples in polynomial time. We empirically observe a significant increase in predictive performance when using maximum common subgraph features instead of correlated local patterns on 60 benchmark datasets from NCI. Moreover, we show that when we randomly sample the pairs of graphs from which to extract the maximum common subgraphs, we obtain a smaller set of features that still allows the same predictive performance as methods that exhaustively enumerate all possible patterns. The sampling strategy turns out to be a very good compromise between a slight decrease in predictive performance (although still remaining comparable with state-of-the-art methods) and a significant runtime reduction (two orders of magnitude on a popular medium size chemoinformatics dataset). This suggests that maximum common subgraphs are interesting and meaningful features.

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Don’t Be Afraid of Simpler Patterns

Cited by 48 publications

References 13 publications

An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules

An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules

Fast Iterative Mining Using Sparsity-Inducing Loss Functions

Effective feature construction by maximum common subgraph sampling

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