Abstract
A nearest neighbor (NN) query is a principal factor in applications that handle multidimensional vector data, such as location-based services, data mining, and pattern recognition. Meanwhile, a nearest neighborhood (NNH) query finds neighborhoods which are not only dense but also near to the query. However, it cannot find desired groups owing to strong restrictions such as fixed group size in previous studies. Thus, in this paper, we propose a dense nearest neighborhood (DNNH) query, which is a query without strong constraints, and three efficient algorithms to solve the DNNH query. The proposed methods are divided into clustering-based and expanding-based methods. The expanding-based method can efficiently find a solution by reducing unnecessary processing using a filtering threshold and expansion breaking criterion. Experiments on various datasets confirm the effectiveness and efficiency of the proposed methods.
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Notes
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Note that the definition of \(\varDelta \) is not same as the one of BNNH.
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Suzuki, H., Chen, H., Furuse, K., Amagasa, T. (2022). Dense Nearest Neighborhood Query. In: Sanfilippo, F., Granmo, OC., Yayilgan, S.Y., Bajwa, I.S. (eds) Intelligent Technologies and Applications. INTAP 2021. Communications in Computer and Information Science, vol 1616. Springer, Cham. https://doi.org/10.1007/978-3-031-10525-8_1
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DOI: https://doi.org/10.1007/978-3-031-10525-8_1
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