Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Learning Distance Measures

  • Carlotta DomeniconiEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_614


Adaptive metric techniques; Flexible metric computation


Many problems in data mining (e.g., classification, clustering, information retrieval) are concerned with the discovery of homogeneous groups of data according to a certain similarity (or distance) measure. The distance measure in use strongly affects the nature of the patterns (clusters, classes, or retrieved images) emerging from the given data. Typically, any chosen fixed distance measure, such as Euclidean or Manhattan distance, does not capture the underlying structure of the data, and fails to find meaningful patterns which correspond to the user’s preferences. To address this issue, techniques have been developed that learn from the data how to compute dissimilarities between pairs of objects. Since objects are commonly represented as vectors of measurements in a given feature space, distances between two objects are computed in terms of the dissimilarity between their corresponding feature components....
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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.George Mason UniversityFairfaxUSA

Section editors and affiliations

  • Dimitrios Gunopulos
    • 1
  1. 1.Department of Computer Science and EngineeringThe University of California at Riverside, Bourns College of EngineeringRiversideUSA