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Combine-and-conquer: improving the diversity in similarity search through influence sampling.

, , , , , and . SAC, page 994-999. ACM, (2015)

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Embedding k-Nearest Neighbor Queries into Relational Database Management Systems., , , , , and . J. Inf. Data Manag., 5 (3): 252-265 (2014)Integrating user preference to similarity queries over medical images datasets., , , , , , and . CBMS, page 486-491. IEEE Computer Society, (2010)Combine-and-conquer: improving the diversity in similarity search through influence sampling., , , , , and . SAC, page 994-999. ACM, (2015)An efficient framework for similarity query optimization., , and . GIS, page 62. ACM, (2007)Algebraic Properties to Optimize kNN Queries., , , , , and . J. Inf. Data Manag., 2 (3): 385-400 (2011)Evaluating the Diversification of Similarity Query Results., , , , , and . J. Inf. Data Manag., 4 (3): 188-203 (2013)Data pre-processing: a new algorithm for feature selection and data discretization., , , and . CSTST, page 252-257. ACM, (2008)Identifying Algebraic Properties to Support Optimization of Unary Similarity Queries., , , , and . AMW, volume 450 of CEUR Workshop Proceedings, CEUR-WS.org, (2009)Parameter-free and domain-independent similarity search with diversity., , , , and . SSDBM, page 5:1-5:12. ACM, (2013)Adding Knowledge Extracted by Association Rules into Similarity Queries., , , , and . J. Inf. Data Manag., 1 (3): 391-406 (2010)