Fast k-clustering queries on embeddings of road networks

Abstract

In this paper, we study the k-clustering query problem on road networks, an important problem in Geographic Information Systems ("GIS"). Using previously developed Euclidean embeddings and reduction to fast nearest neighbor search, we show and analyze approximation algorithms for these problems. Since these problems are difficult to solve exactly -and even hard to approximate for most variantswe compare our constant factor approximation algorithms to exact answers on small synthetic datasets and on a dataset representing Tallahassee, Florida, a small city. We have implemented a web application that demonstrates our method for road networks in the same small city.

Geometric Clustering and its Applications

“…Using Euclidean embeddings, and reduction to fast nearest neighbor search, we devise an approximation algorithm for this problem. Our initial experiments on real world data sets indicate fast computation of constant factor approximate solutions for query sets much larger than previously computable using exact techniques [3,16].…”

Operadores físicos binários para consultas por similaridade em SGBDR

“…1. Introdução que executa a consulta [62,38,18]. Apesar dos operadores de junção por similaridade realizarem a recuperação dos objetos mais similares, o quinto problema destes operadores é que, em geral, eles retornam um conjunto resposta de cardinalidade elevada.…”