Structural Entropic Difference: A Bounded Distance Metric for Unordered Trees

Connor, Richard; Simeoni, Fabio; Iakovos, Michael

doi:10.1109/sisap.2009.29

Cited by 2 publications

(3 citation statements)

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“…It has been previously shown how the ensemble may be used to effect as a characteristic of unordered trees [3,4]; the contribution of this paper is to extend the context of that work, and show that many other classes of data can be addressed with a similar treatment.…”

Section: Introductionmentioning

confidence: 93%

“…Thus for example {a, a, b}, {b, b, b, c, c, c, c} → (2, 1, 0), (0, 3,4) Having done this, then Cosine Similarity (CS, the cosine of the angle between the vectors) gives a useful similarity metric bounded by [ 0, 1] which is tolerant to the relative magnitudes of the operands. However the Cosine Distance (CD = 1 − CS) does not preserve triangle inequality.…”

Section: Distance Over Multisetsmentioning

confidence: 99%

“…3 in fact, it is NP-hard The union of all attributes was then encoded into a mobile interface, allowing the user to identify whichever happen to be visible or otherwise known, even when others are not: for example, much can be deduced only from a feeding habitat and a behaviour pattern, even when a good look at the bird has not been achieved. As the user fills in the interface, a new tree representing a prototype of the bird being observed is created, and incrementally maintained as more data is added.…”

Section: Species Descriptorsmentioning

confidence: 99%

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Towards a universal information distance for structured data

Connor

Simeoni

Iakovos

et al. 2011

Proceedings of the Fourth International Conference on SImilarity Search and APplications

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The similarity of objects is one of the most fundamental concepts in any collection of complex information; similarity, along with techniques for storing and indexing sets of values based on it, is a concept of ever increasing importance as inherently unordered data sets become ever more common. Examples of such datasets include collections of images, multimedia, and semi-structured data. There are however two, largely separate, classes of related research. On the one hand, techniques such as clustering and similarity search give general treatments over sets of data. Results are domain-independent, typically relying only on the existence of an anonymous distance metric over the set in question. On the other hand, results in the domain of similarity measurement are often limited to the context of pairwise comparison over individual objects, and are not typically set in a wider context. Published algorithms are scattered over various demand-led subject areas, including for example bioinformatics, library sciences, and crime detection. Few, if any, of the published algorithms have the distance metric properties. We have identified a distance metric, Ensemble Distance, which we believe can help to bridge this gap. Ensemble Distance is a non-Euclidean distance metric which we believe can be used in the treatment of many classes of structured data. For any complex type where a useful characterisation exists in the form of an ensemble, we can produce a distance metric for that type. This will in turn allow use of the complex type within off-the-shelf clustering and similarity search algorithms; this would be a major result in the management of complex data sets

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Section: Introductionmentioning

confidence: 93%

Section: Distance Over Multisetsmentioning

confidence: 99%

Section: Species Descriptorsmentioning

confidence: 99%

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