Spatial clustering methods in data mining
Spatial clustering is the process of grouping a set of objects into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are dissimilar to objects in other clusters. As a branch of statistics, cluster analysis has been studied extensively for many years, focusing mainly on distance-based cluster analysis. Cluster analysis tools based on k-means, k-medoids, and several other methods have also been built into many statistical analysis software packages or systems, such as S-Plus, SPSS, and SAS. Clustering has also been studied in the field of machine learning as a type of unsupervised learning because it does not rely on predefined class and classlabelled training examples. However, efforts to perform effective and efficient clustering on large databases only started in recent years with the emergence of data mining.