### Abstract

Language | English |
---|---|

Title of host publication | SISAP '09: Proceedings of the 2009 Second International Workshop on Similarity Search and Applications |

Pages | 21-29 |

Number of pages | 9 |

DOIs | |

Publication status | Published - 2009 |

### Fingerprint

### Keywords

- structural entropic difference
- unordered trees
- computer science

### Cite this

*SISAP '09: Proceedings of the 2009 Second International Workshop on Similarity Search and Applications*(pp. 21-29) https://doi.org/10.1109/SISAP.2009.29

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*SISAP '09: Proceedings of the 2009 Second International Workshop on Similarity Search and Applications.*pp. 21-29. https://doi.org/10.1109/SISAP.2009.29

**Structural entropic difference: a bounded distance metric for unordered trees.** / Connor, R.; Simeoni, F.; Iakovos, M.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Structural entropic difference: a bounded distance metric for unordered trees

AU - Connor, R.

AU - Simeoni, F.

AU - Iakovos, M.

PY - 2009

Y1 - 2009

N2 - We show a new metric for comparing unordered, tree-structured data. While such data is increasingly important in its own right, the methodology underlying the construction of the metric is generic and may be reused for other classes of ordered and partially ordered data. The metric is based on the information content of the two values under consideration, which is measured using Shannon's entropy equations. In essence, the more commonality the values possess, the closer they are. As values in this domain may have no commonality, a good metric should be bounded to represent this. This property has been achieved, but is in tension with triangle inequality.

AB - We show a new metric for comparing unordered, tree-structured data. While such data is increasingly important in its own right, the methodology underlying the construction of the metric is generic and may be reused for other classes of ordered and partially ordered data. The metric is based on the information content of the two values under consideration, which is measured using Shannon's entropy equations. In essence, the more commonality the values possess, the closer they are. As values in this domain may have no commonality, a good metric should be bounded to represent this. This property has been achieved, but is in tension with triangle inequality.

KW - structural entropic difference

KW - unordered trees

KW - computer science

UR - http://portal.acm.org/ft_gateway.cfm?id=1638178&type=pdf&coll=Portal&dl=GUIDE&CFID=76298374&CFTOKEN=30615543

U2 - 10.1109/SISAP.2009.29

DO - 10.1109/SISAP.2009.29

M3 - Conference contribution book

SP - 21

EP - 29

BT - SISAP '09: Proceedings of the 2009 Second International Workshop on Similarity Search and Applications

ER -