Recently, permutation based indexes have attracted interest in the area of similarity search. The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots (or reference objects). Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query. This, of course assumes that similar objects are represented by similar permutations of the pivots. In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot selection strategies do not reveal better performance. However, to the best of our knowledge, no rigorous comparison has been performed yet. In this paper we compare five pivots selection strategies on three permutation-based similarity access methods. Among those, we propose a novel strategy specifically designed for permutations. Two significant observations emerge from our tests. First, random selection is always outperformed by at least one of the tested strategies. Second, there is not a strategy that is universally the best for all permutation-based access methods; rather different strategies are optimal for different methods. © 2013 Springer-Verlag.
Amato, G., Esuli, A., & Falchi, F. (2013). Pivot selection strategies for permutation-based similarity search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8199 LNCS, pp. 91–102). https://doi.org/10.1007/978-3-642-41062-8_10