Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Generalized Substructures from a Set of Labeled Graphs
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Large scale mining of molecular fragments with wildcards
Intelligent Data Analysis
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Optimizing Feature Sets for Structured Data
ECML '07 Proceedings of the 18th European conference on Machine Learning
Large-scale graph mining using backbone refinement classes
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Augmenting the generalized hough transform to enable the mining of petroglyphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Pattern mining methods for graph data have largely been restricted to ground features, such as frequent or correlated subgraphs. Kazius et al. have demonstrated the use of elaborate patterns in the biochemical domain, summarizing several ground features at once. Such patterns bear the potential to reveal latent information not present in any individual ground feature. However, those patterns were handcrafted by chemical experts. In this paper, we present a data-driven bottom-up method for pattern generation that takes advantage of the embedding relationships among individual ground features. The method works fully automatically and does not require data preprocessing (e.g., to introduce abstract node or edge labels). Controlling the process of generating ground features, it is possible to align them canonically and merge (stack) them, yielding a weighted edge graph. In a subsequent step, the subgraph features can further be reduced by singular value decomposition (SVD). Our experiments show that the resulting features enable substantial performance improvements on chemical datasets that have been problematic so far for graph mining approaches.