The Fractal Dimension Making Similarity Queries More Efficient

This paper presents a new algorithm to answer k -nearest neighbor queries called the Fractal k -Nearest Neighbor (k NNF ()). This algorithm takes advantage of the fractal dimension of the dataset under scan to estimate a suitable radius to shrinks a query that retrieves the k -nearest neighbors of a query object. k -NN() algorithms starts searching for elements at any distance from the query center, progressively reducing the allowed distance used to consider elements as worth to analyze. If a proper radius can be set to start the process, a significant reduction in the number of distance calculations can be achieved. The experiments performed with real and synthetic datasets over the access method Slim-tree, have shown that the efficiency of our approach makes the total processing time to drop up to 50%, while requires 25% less distance calculations.