Just as in many other computational problems, it is possible to trade core storage and computation time. In experimental IR, computation time is likely to be at a premium and a classification process can usually be speeded up by using extra storage.

One important decision to be made in any retrieval system concerns the organisation of storage. Usually part of the file structure will be kept in fast store and the rest on backing store. In experimental IR we are interested in a flexible system and getting experiments done quickly. Therefore, frequently much or all of a classification structure is kept in fast store although this would never be done in an operational system where the document collections are so much bigger.

Another good example of the difference in approach between experimental and operational implementations of a classification is in the permanence of the cluster representatives. In experiments we often want to vary the cluster representatives at search time. In fact, we require that each cluster representative can be quickly specified and implemented at search time. Of course, were we to design an operational classification, the cluster representatives would be constructed once and for all at cluster time.

Probably one of the most important features of a classification implementation is that it should be able to deal with a changing and growing document collection. Adding documents to the classification should not be too difficult. For instance, it should not be necessary to take the document classification 'off the air' for lengthy periods to update it. So, we expect the classification to be designed in such a way that a new batch of documents can be readily inserted without reclassifying the entire set of both old and new documents.

Although many classification algorithms claim this feature, the claim is almost invariably not met. Because of the heuristic nature of many of the algorithms, the updated classification is not the same as it would have been if the increased set had been classified from scratch. In addition, many of the updating strategies mess up the classification to such an extent that it becomes necessary to throw away the classification after a series of updates and reclassify completely.

These comments tend to apply to the n log n classification methods. Unfortunately, they are usually recommended over the n^2 methods for two reasons. Firstly, because n log n is considerably less than n^2, and secondly because the time increases only as log n for the n log n methods but as n for the n^2 methods. On the face of it these are powerful arguments. However, I think they mislead. If we assume that the n log n methods cannot be updatedwithout reclassifying each time and that the n[2] methods can (for example, single-link), thenthe correct