@misc{10481/77906,
year = {2008},
url = {https://hdl.handle.net/10481/77906},
abstract = {Conventional approaches to similarity search and case-based
retrieval, such as nearest neighbor search, require the speci cation of a
global similarity measure which is typically expressed as an aggregation
of local measures pertaining to di erent aspects of a case. Since the
proper aggregation of local measures is often quite di cult, we propose a
novel concept called similarity skyline. Roughly speaking, the similarity
skyline of a case base is de ned by the subset of cases that are most
similar to a given query in a Pareto sense. Thus, the idea is to proceed
from a d-dimensional comparison between cases in terms of d (local)
distance measures and to identify those cases that are maximally similar
in the sense of the Pareto dominance relation [2]. To re ne the retrieval
result, we propose a method for computing maximally diverse subsets of
a similarity skyline. Moreover, we propose a generalization of similarity
skylines which is able to deal with uncertain data described in terms of
interval or fuzzy attribute values. The method is applied to similarity
search over uncertain archaeological data.},
publisher = {Springer},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Supporting case-based retrieval by similarity skylines: Basic concepts and extensions},
author = {Hüllermeier, Eyke and Prados Suárez, María Belén},
}