  We introduce K-tree in an information retrieval context. It is an efficient
approximation of the k-means clustering algorithm. Unlike k-means it forms a
hierarchy of clusters. It has been extended to address issues with sparse
representations. We compare performance and quality to CLUTO using document
collections. The K-tree has a low time complexity that is suitable for large
document collections. This tree structure allows for efficient disk based
implementations where space requirements exceed that of main memory.

Information Retrieval;Artificial Intelligence;Data Structures and Algorithms