Similarity Search in Metric Spaces

Similarity search refers to any searching problem which retrieves objects from a set that are close to a given query object as re ected by some similarity criterion. It has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. In this thesis, we examine algorithms designed for similarity search over arbitrary metric spaces rather than restricting ourselves to vector spaces. The contributions in this paper include the following: First, after de ning pivot sharing and pivot localization, we prove probabilistically that pivot sharing level should be increased for scattered data while pivot localization level should be increased for clustered data. This conclusion is supported by extensive experiments. Moreover, we proposed two new algorithms, RLAESA and NGH-tree. RLAESA, using high pivot sharing level and low pivot localization level, outperforms the fastest algorithm in the same category, MVP-tree. NGH-tree is used as a framework to show the e ect of increasing pivot sharing level on search e ciency. It provides a way to improve the search e ciency in almost all algorithms. The experiments with RLAESA and NGH-tree not only show their preformance, but also support the rst conclusion we mentioned above. Second, we analyzed the issue of disk I/O on similarity search and proposed a new algorithm SLAESA to improve the search e ciency by switching random I/O access to sequential I/O access.