Efficient Hierarchical Clustering of Large Data Sets Using P-trees.
Anne M Denton, Qiang Ding, William Perrizo, Qin Ding · 2002
Hierarchical clustering methods have attracted much attention by giving the user a maximum amount of flexibility. Rather than requiring parameter choices to be predetermined, the result represents all possible levels of granularity. In this paper a hierarchical method is introduced that is fundamentally related to partitioning methods, such as k-medoids and k-means as well as to a density based method, namely center-defined DENCLUE. It is superior to both kmeans and k-medoids in its reduction of outlier influence. Nevertheless it avoids both the time complexity of some partition-based algorithms and the storage requirements of density-based ones. An implementation is presented that is particularly suited to spatial-, stream-, and multimedia data, using P-trees for efficient data storage and access.