In many database applications, one of the common queries is to find approximate matches to a given query item from a collection of data items. For example, given an image database, one may want to retrieve all images that are similar to a given query image. Distance based index structures

Abstract. Data structures for similarity search are commonly evalu-ated on data in vector spaces, but distance-based data structures are also applicable to non-vector spaces with no natural concept of dimen-sionality. The intrinsic dimensionality statistic of Chávez and Navarro provides a way

. In this paper, we propose a distance-based index structure, Muse (Multilevel distancebased index structure for Eros), for efficient retrieval of MTS items using Eros. Muse constructs each level as a distance-based index structure without using the weights, up to z levels. At the query time, Muse combines the z

distance function determined by the application domain. The most popular string distance measures are based on (a weighted) count of (i) character edit or (ii) block edit operations to transform one string into the other. Examples include the Levenshtein edit distance and the recently introduced

In this paper we investigate data structures for performing similarity search queries in metric spaces. We have selected six methods proposed in three dierent communities: two theoretically analyzed structures of Clarkson, GNAT and SA-tree from databases, and AESA and LAESA which are used for pattern recognition applications. We have implemented all these methods and compared their eciency in practice. We propose an improvement for GNAT data structure that reduces its space requirement. We show this improvement does not degrade the query time by much.

In this paper, we propose a Distance-based Sequence In-dexing Method (DSIM) for indexing and searching genome databases. Borrowing the idea of video compression, we compress the genomic sequence database around a set of automatically selected reference words, formed from high-frequency data

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Similarity search leveraging distance-based index structures is increasingly being used for complex data types. It has been shown that for high dimensional uniform vectors with similarity norms, any clustering and partitioning index method is outperformed by sequential scan. However, intrinsic

Similarity search leveraging distance-based index structures is increasingly being used for both multimedia and biological database applications. We consider distance-based indexing for three important biological data types, protein k-mers with the metric PAM model, DNA k-mers with Hamming distance

that make the general assumption that similarity is represented with a distance metric d. Existing methods for handling similarity search in this setting typically fall into one of two classes. The first directly indexes the objects based on distances (distance-based indexing), while the second is based