Bulk-loading Dynamic Metric Access Methods

The main contribution of this paper is a bulk-loading algorithm for multi-way dynamic metric access methods based on the covering radius of a rep- resentative, like the Slim-tree. The proposed algorithm is sample-based, and it builds a height-balanced tree in a top-down fashion, using the metric domain's distance function and a bound limit to group and determine the number of ele- ments in each partition of the dataset at each step of the algorithm. Experiments performed to drill its performance shows that our bulk-loading method is up to 6 times faster to build a tree than the sequential insertion method regarding construction time, and that it improves the search performance too.