Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts and Extensions

Conventional approaches to similarity search and case-based retrieval, such as nearest neighbor search, require the specification of a global similarity measure which is typically expressed as an aggregation of local measures pertaining to different aspects of a case. Since the proper aggregation of local measures is often quite difficult, we propose a novel concept called similarity skyline. Roughly speaking, the similarity skyline of a case base is defined 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 refine 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 te...

• Type: Conference
• Year: 2008
• Where: EWCBR
• Authors: Eyke Hüllermeier, Ilya Vladimirskiy, Belén Prados-Suárez, Eva Stauch
• Keywords: Automated Reasoning, EWCBR 2008, Global Similarity Measure, Similarity Search, Similarity Skylines