Emilios Cambouropoulos
(Faculty of Music, University of Edinburgh)
Alan Smaill
(Department of Artificial Intelligence, University of Edinburgh)
Abstract
In this paper, a working formal definition will be given according to which
similarity a) is contextually defined, b) may be applied to any property
ascribed to an entity (not only to perceptual properties such as visual
appearance) and c) has an associated notion of corresponding categories.
This definition inextricably binds together similarity and categorisation
in such a way that changes in similarity ratings between entities result
in category changes, and vice versa. In line with the above descriptions,
the Unscramble algorithm will be presented which, given a set of objects
and an initial set of properties, generates a range of plausible
classifications for a given context. During this dynamically evolving
process the initial set of properties is altered/adjusted so that a
'satisfactory' description is generated (taking into account general
cognitive principles such as economy and informativeness). There is no
need to determine in advance an initial number of classes nor is there
a need to reach a strictly well-formed (e.g. non-overlapping) description.
At every stage of the process both the extension and the intension of the
emerging categories is explicitly defined. One example will be presented
that illustrates the capabilities and efficiency of the model. As the
proposed algorithm has been developed mainly to accommodate problems of
musical similarity and categorisation it is necessary to test it further
on data sets from different domains and compare it to other relevant machine
learning algorithms.