On the Expressivity of Alignment-Based Distance and Similarity Measures on Sequences and Trees in Inducing Orderings

Autor: Martin Emms, Hector-Hugo Franco-Penya
Rok vydání: 2012
Předmět:
Zdroj: Springer Proceedings in Mathematics & Statistics ISBN: 9781461450757
DOI: 10.1007/978-1-4614-5076-4_1
Popis: Both ‘distance’ and ‘similarity’ measures have been proposed for the comparison of sequences and for the comparison of trees, based on scoring mappings. For a given alphabet of node-labels, the measures are parameterised by a table giving label-dependent values for swaps, deletions and insertions. The paper addresses the question whether an ordering by a ‘distance’ measure, with some parameter setting, can be also expressed by a ‘similarity’ measure, with some other parameter setting, and vice versa. Ordering of three kinds is considered: alignment-orderings, for fixed source S and target T, neighbour-orderings, where for a fixed S, varying candidate neighbours T i are ranked, and pair-orderings, where for varying S i , and varying T j , the pairings \(\langle {S}_{i},{T}_{j}\rangle\) are ranked. We show that (1) any alignment-ordering expressed by ‘distance’ setting be re-expressed by a ‘similarity’ setting, and vice versa; (2) any neigbour-ordering and pair-ordering expressed by a ‘distance’ setting be re-expressed by a ‘similarity’ setting; (3) there are neighbour-orderings and pair-orderings expressed by a ‘similarity’ setting which cannot be expressed by a ‘similarity’ setting. A consequence of this is that there are categorisation and hierarchical clustering outcomes which can be achieved via similarity but not via
Databáze: OpenAIRE