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Extracting and Summarizing the Frequent Emerging Graph Patterns from a Dataset of Graphs
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Poezevara, Guillaume Cuissart, Bertrand Crémilleux, Bruno |
| Abstract | 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. |
| Related Links | https://hal.science/hal-01018410/file/RIACL-POEZEVARA-2011-1.pdf |
| ISSN | 09259902 |
| Issue Number | 3 |
| Volume Number | 37 |
| Journal | Journal of Intelligent Information Systems |
| e-ISSN | 15737675 |
| Language | English |
| Publisher | HAL CCSD Springer Verlag |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Condensed representation Chemical information Data mining Subgraph isomorphism Emerging patterns |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Networks and Communications Computer Science Hardware and Architecture Software Information Systems |
