Titlebook: Similarity Search and Applications; 16th International C Oscar Pedreira,Vladimir Estivill-Castro Conference proceedings 2023 The Editor(s)

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书目名称Similarity Search and Applications影响因子(影响力)




书目名称Similarity Search and Applications影响因子(影响力)学科排名




书目名称Similarity Search and Applications网络公开度




书目名称Similarity Search and Applications网络公开度学科排名




书目名称Similarity Search and Applications被引频次




书目名称Similarity Search and Applications被引频次学科排名




书目名称Similarity Search and Applications年度引用




书目名称Similarity Search and Applications年度引用学科排名




书目名称Similarity Search and Applications读者反馈




书目名称Similarity Search and Applications读者反馈学科排名

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0302-9743 in A Coruña, Spain, during October 9–11, 2023..The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, f

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Accelerating ,-Means Clustering with Cover Treesnge, than previous approaches based on the k-d tree. By combining this with upper and lower bounds, as in state-of-the-art approaches, we obtain a hybrid algorithm that combines the benefits of tree aggregation and bounds-based filtering.

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Solving ,-Closest Pairs in High-Dimensional Datament our theoretical analysis with an experimental evaluation, showing that our approach can provide solutions orders of magnitude faster than current state-of-the-art data structures designed for specific metrics.

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Class Representatives Selection in Non-metric Spaces for Nearest Prototype Classificationepresentatives that ensure sufficient class coverage. Thanks to the graph-based approach, CRS can be applied to any space where at least a pairwise similarity can be defined. In the experimental evaluation, we demonstrate that our method outperforms the state-of-the-art techniques on multiple datasets from different domains.

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Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensionsrity, we offer in-depth algorithmic and experimental evaluations. Our findings highlight TS’s unique attributes and confirm its performance, surpassing sequential scans by 1.7x at perfect recall and a remarkable 2.5x at 97% recall.

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Finding HSP Neighbors via an Exact, Hierarchical Approachn in strings, enhancing .NN classification, simplifying chemical networks, estimating local intrinsic dimensionality, and generating uniform samples from skewed distributions, among others. However, the linear complexity of finding HSP neighbors of a query limit its scalability, except when sacrific

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