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In a data collection M, the nearest neighbor to a data object q is the data object M i , which minimizes dist (q, M i ), where dist is a distance measure defined for the objects in question. Note that the fact that the object M i is the nearest neighbor to q does not imply that q is the nearest neighbor to M i .
Motivation and Background
Nearest neighbors are useful in many machine learning and data mining tasks, such as classification, anomaly detection and motif discovery and in more general tasks such as spell checking, vector quantization, plagiarism detection, web search, and recommender systems.
The naive method to find the nearest neighbor to a point q requires a linear scan of all objects in M. Since this may be unacceptably slow for large datasets and/or computationally demanding distance measures, there is a huge amount of literature on speeding up nearest neighbor searches (query-by-content). The fastest methods depend...
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Recommended Reading
Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on management of data. ACM, New York, pp 47–57. ISBN: 0-89791-128-8
Manolopoulos Y, Nanopoulos A, Papadopoulos AN, Theodoridis Y (2005) R-trees: theory and applications. Springer, Berlin
Zezula P, Amato G, Dohnal V, Batko M (2005) Similarity search: the metric space approach. In: Advances in database systems, vol 32. Springer, New York, p 220. ISBN:0-387-29146-6
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Keogh, E. (2017). Nearest Neighbor. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_579
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_579
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