Efficient and Effective Clustering Methods for Spatial Data Mining

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 useCLAHANS. Our analysis and experiments show that with the assistanceof 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.

• Type: Conference
• Year: 1994
• Where: VLDB
• Authors: Raymond T. Ng, Jiawei Han
• Keywords: Clustering Methods, Data Mining Algorithms, Database, Spatial Data Mining, VLDB 1994