An Improved Algorithm for Segregating Large Geospatial Data
Kara E. Scott, Tonny Justus Oyana · 2006
SUMMARY This study investigates an improved k-means clustering algorithm for segregating large geospatial data. Although the conventional k-means method is sufficient for datasets with minimal data, it does not perform well and, therefore yields poor accuracy for high-volume datasets. Clustering methods are one of the most important components in data classification, visualization, and mining highvolume datasets. The primary aim of this study is to explore two individual methods that were originally designed to increase the overall performance of k-means clustering: Mashor's updating method and the Davies-Bouldin validity index.