Latent Structure Pattern Mining

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 introtract 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 th...

• Type: Journal
• Year: 2010
• Where: PKDD
• Authors: Andreas Maunz, Christoph Helma, Tobias Cramer, Stefan Kramer
• Keywords: Data Mining, Ground Features, Individual Ground, Individual Ground Feature, PKDD 2010