efficiency of implementation for a particular application.
A compromise can be adopted in which the classification methods generates overlapping classes in the first instance and is finally 'tidied up' to give exclusive classes.
An example of an ordered classification is a hierarchy.
The classes are ordered by inclusion, i.e. the classes at one level are nested in the classes at the next level.
To give a simple example of unordered classification is more difficult.
Unordered classes generally crop up in automatic thesaurus construction.
The classes sought for a thesaurus are those which satisfy certain homogeneity and isolation conditions but in general cannot be simply related to each other.
(See for example the use and definition of clumps in Needham.) For certain applications ordering is irrelevant, whereas for others such as document clustering it is of vital importance.
The ordering enables efficient search strategies to be devised.
The discussion about classification has been purposely vague up to this point.
Although the break down scheme discussed gives some insight into classification methods, it is of little use when discussing any particular classification method. Like all categorisations it isolates some ideal types; but any particular instance will often fall between categories or be a member of a large proportion of categories.
Let me know be more specific about current (and past) approaches to classification, particularly in the context of information retrieval.
The cluster hypothesis
Before describing the battery of classification methods that are now used in information retrieval, I should like to discuss the underlying hypothesis for their use in document clustering.
This hypothesis may be simply stated as follows: closely associated documents tend to be relevant to the same requests. I shall refer to this hypothesis as the Cluster Hypothesis.
A basic assumption in retrieval systems is that documents relevant to a request are separated from those which are not relevant, i.e. that the relevant documents are more like one another than they are like non-relevant documents.
Whether this is true for a collection can be tested as follows.
Compute the association between all pairs of documents:
(a) both of which are relevant to a request, and
(b) one of which is relevant and the other non-relevant.
Summing over a set of requests gives the relative distribution of