Efficient and Effective Clustering Methods for Spatial Data Mining

TLDR

Spatial data mining seeks to uncover implicit relationships and characteristics within spatial databases. The paper investigates whether clustering methods can aid spatial data mining and introduces CLAHANS, a randomized‑search clustering algorithm. The authors develop CLAHANS and two spatial data mining algorithms that incorporate it. Experiments show that CLAHANS enables the new algorithms to discover difficult‑to‑find spatial patterns and outperforms existing clustering methods in efficiency.

Abstract

Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLAHANS which is based on randomized search. We also develop two spatial data mining algorithms that use CLAHANS. Our analysis and experiments show that with the assistance of CLAHANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms. Furthermore, experiments conducted to compare the performance of CLAHANS with that of existing clustering methods show that CLAHANS is the most efficient.