iDistance: An adaptive B+-tree based indexing method for nearest neighbor search

643 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Spherical hashing (2012, methodology): “[12] used k-means clustering and Venkateswaran et al.”
Efficient Processing of k Nearest Neighbor Joins using MapReduce (2012, methodology): “Similar to our proposed methods, by splitting the two input datasets into respective set of partitions, IJoin method employs a B + -tree to maintain the objects of each dataset using the iDistance technique [20, 9] and answer k NN join based on the properties of B + -tree.”
Singleton indexes for nearest neighbor search (2016, background): “This strategy resembles iDistance [14], which is designed for multidimensional data.”
Machine learning for Internet of Things data analysis: A survey (2017, background): “In [59], the authors have addressed this issue by constructing a tree-based search with a one-off computation.”
Distance based similarity models for content based multimedia retrieval (2013, background): “, 2005], the iDistance [Jagadish et al., 2005], and the M-index [Novak et al.”
Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph (2017, methodology): “For dense continuous vectors, various solutions have been proposed such as tree-structure based approaches [2,6,8,17,24,36], hashing-based approaches [18, 20, 23, 32, 40], quantization-based approaches [1, 19, 26, 39], and graph-based approaches [3, 21, 33, 41].”