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 representative, 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 elements 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.