Rank Aggregation of Candidate Sets for Efficient Similarity Search

  • David Novak
  • Pavel Zezula
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8645)


Many current applications need to organize data with respect to mutual similarity between data objects. Generic similarity retrieval in large data collections is a tough task that has been drawing researchers’ attention for two decades. A typical general strategy to retrieve the most similar objects to a given example is to access and then refine a candidate set of objects; the overall search costs (and search time) then typically correlate with the candidate set size. We propose a generic approach that combines several independent indexes by aggregating their candidate sets in such a way that the resulting candidate set can be one or two orders of magnitude smaller (while keeping the answer quality). This achievement comes at the expense of higher computational costs of the ranking algorithm but experiments on two real-life and one artificial datasets indicate that the overall gain can be significant.


Search Time Voronoi Cell Query Time Probe Depth Candidate Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Novak
    • 1
  • Pavel Zezula
    • 1
  1. 1.Masaryk UniversityBrnoCzech Republic

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