Clustering in massive data sets

We review the time and storage costs of search and clustering algorithms. We exemplify these, based on case-studies in astronomy, information retrieval, visual user interfaces, chemical databases, and other areas. Theoretical results developed as far back as the 1960s still very often remain topical. More recent work is also covered in this article. This includes a solution for the statistical question of how many clusters there are in a dataset. We also look at one line of inquiry in the use of clustering for human-computer user interfaces. Finally, the visualization of data leads to the consideration of data arrays as images, and we speculate on future results to be expected here. `Now', said Rabbit, `this is a Search, and I've Organised it {' `Done what to it? ' said Pooh. `Organised it. Which means { well, it's what you do to a Search