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Extracting and summarizing the frequent emerging graph patterns from a dataset of graphs

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Emerging patterns are patterns of great interest for discovering information from data and characterizing classes. Mining emerging patterns remains a challenge, especially with graph data. In this paper, we propose a method to mine the whole set of frequent emerging graph patterns, given a frequency threshold and an emergence threshold. Our results are achieved thanks to a change of the description of the initial problem so that we are able to design a process combining efficient algorithmic and data mining methods. Moreover, we show that the closed graph patterns are a condensed representation of the frequent emerging graph patterns and we propose a new condensed representation based on the representative pruned graph patterns: by providing shorter patterns, it is especially dedicated to represent a set of graph patterns. Experiments on a real-world database composed of chemicals show the feasibility and the efficiency of our approach.

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The authors would like to thank Arnaud Soulet for very fruitful discussions and the Music-dfs prototype and the CERMN lab for its invaluable help about the data and the chemical knowledge. This work is partly supported by the ANR (French Research National Agency) funded Innotox, Bingo2 projects and the Region Basse-Normandie (Innotox2 project).

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Correspondence to Bruno Crémilleux.

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Poezevara, G., Cuissart, B. & Crémilleux, B. Extracting and summarizing the frequent emerging graph patterns from a dataset of graphs. J Intell Inf Syst 37, 333 (2011). https://doi.org/10.1007/s10844-011-0168-1

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  • Data mining
  • Emerging patterns
  • Condensed representation
  • Subgraph isomorphism
  • Chemical information