Clustering-based similarity search in metric spaces with sparse spatial centers

Authors:
Nieves Brisaboa;Oscar Pedreira;Diego Seco;Roberto Solar;Roberto Uribe
Affiliations:
Database Laboratory, University of A Coruña, A Coruña, Spain;Database Laboratory, University of A Coruña, A Coruña, Spain;Database Laboratory, University of A Coruña, A Coruña, Spain;Dpto. Ingeniería en Computación, Universidad de Magallanes, Punta Arenas, Chile and Grupo de Bases de Datos, Universidad Nacional de la Patagonia Austral, Santa Cruz, Argentina;Dpto. Ingeniería en Computación, Universidad de Magallanes, Punta Arenas, Chile and Grupo de Bases de Datos, Universidad Nacional de la Patagonia Austral, Santa Cruz, Argentina
Venue:
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
Year:
2008

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Abstract

Metric spaces are a very active research field which offers efficient methods for indexing and searching by similarity in large data sets. In this paper we present a new clustering-based method for similarity search called SSSTree. Its main characteristic is that the centers of each cluster are selected using Sparse Spatial Selection (SSS), a technique initially developed for the selection of pivots. SSS is able to adapt the set of selected points (pivots or cluster centers) to the intrinsic dimensionality of the space. Using SSS, the number of clusters in each node of the tree depends on the complexity of the subspace it represents. The space partition in each node will be made depending on that complexity, improving thus the performance of the search operation. In this paper we present this new method and provide experimental results showing that SSSTree performs better than previously proposed indexes.