SAHN Clustering in Arbitrary Metric Spaces Using Heuristic Nearest Neighbor Search

2014 ◽  
pp. 90-101 ◽  
Author(s):  
Nils Kriege ◽  
Petra Mutzel ◽  
Till Schäfer
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search

Nearest neighbor search in metric spaces through Content-Addressable Networks

2008 ◽  
Vol 44 (1) ◽  
pp. 411-429 ◽  
Author(s):  
Fabrizio Falchi ◽  
Claudio Gennaro ◽  
Pavel Zezula
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search

Antipole tree indexing to support range search and k-nearest neighbor search in metric spaces

2005 ◽  
Vol 17 (4) ◽  
pp. 535-550 ◽  
Author(s):  
D. Cantone ◽  
A. Ferro ◽  
A. Pulvirenti ◽  
D.R. Recupero ◽  
D. Shasha
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Range Search ◽  
Neighbor Search ◽  
K Nearest Neighbor Search

Nearest neighbor search in metric spaces through Content-Addressable Networks

2007 ◽  
Vol 43 (3) ◽  
pp. 665-683 ◽  
Author(s):  
Fabrizio Falchi ◽  
Claudio Gennaro ◽  
Pavel Zezula
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search

scholarly journals Reverse Nearest Neighbor Search in Metric Spaces

2006 ◽  
Vol 18 (9) ◽  
pp. 1239-1252 ◽  
Author(s):  
Yufei Tao ◽  
Man Lung Yiu ◽  
N. Mamoulis
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Reverse Nearest Neighbor ◽  
Neighbor Search

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

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