A case-based approach to intelligent information retrieval
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
An Efficient Branch-and-bound Algorithm for Finding a Maximum Clique with Computational Experiments
Journal of Global Optimization
Preferences in AI: An overview
Artificial Intelligence
On different types of fuzzy skylines
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
On possibilistic skyline queries
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Fuzzy rule-based similarity model enables learning from small case bases
Applied Soft Computing
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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 terms of interval or fuzzy attribute values. The method is applied to similarity search over uncertain archaeological data.