Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Clustering of multivariate data is a commonly used technique in ecology, and many approaches to clustering are available. The results from a clustering algorithm are uncertain, but few clustering approaches explicitly acknowledge this uncertainty. One exception is Bayesian mixture modelling, which treats all results probabilistically, and allows comparison of multiple plausible classifications of the same data set. We used this method, implemented in the AutoClass program, to classify catchments (watersheds) in the Murray Darling Basin (MDB), Australia, based on their physiographic characteristics (e.g. slope, rainfall, lithology). The most likely classification found nine classes of catchments. Members of each class were aggregated geographically within the MDB. Rainfall and slope were the two most important variables that defined classes. The second-most likely classification was very similar to the first, but had one fewer class. Increasing the nominal uncertainty of continuous data resulted in a most likely classification with five classes, which were again aggregated geographically. Membership probabilities suggested that a small number of cases could be members of either of two classes. Such cases were located on the edges of groups of catchments that belonged to one class, with a group belonging to the second-most likely class adjacent. A comparison of the Bayesian approach to a distance-based deterministic method showed that the Bayesian mixture model produced solutions that were more spatially cohesive and intuitively appealing. The probabilistic presentation of results from the Bayesian classification allows richer interpretation, including decisions on how to treat cases that are intermediate between two or more classes, and whether to consider more than one classification. The explicit consideration and presentation of uncertainty makes this approach useful for ecological investigations, where both data and expectations are often highly uncertain.

Assessment frameworks : a critical reflection

Increasing widespread requirements that assessment practice conforms to generic guidelines contained in assessment frameworks has been contentious and critiques offered on individual frameworks have been assumed to apply to the concept of assessment frameworks more generally. After comparing four assessment frameworks currently being used in the UK, this paper argues that although some generalizations can be made, for the most part, they are highly individual documents in terms of range and depth of content, the extent to which they are evidenced and the quality of that evidence and implicit expectations as to the skill bases of assessors. Furthermore, the introduction of assessment frameworks is not in itself a panacea to ensure good practice. Even with the most comprehensive frameworks, social workers will still need comprehensive training in assessment and supervision of their practice.

Electronic commerce strategic investment justification frameworks : an Australian perspective

Strategic use of information technology, especially electronic commerce, has been used by organisations throughout the world in a vast array of industries to gain a competitive industry. With growing interest in electronic commerce organisations are now developing a new range of electronic commerce applications. However, justification in allocating organisation resources does not always follow the more commonly accepted methods. This paper explores why organizations invest in electronic commerce applications and highlights several approaches to justification. Central to the work was to determine the underlying benefits of investing in Web applications. This paper examines various models and frameworks that can be used as a form of justification. A conclusion of this paper is a framework for the justification of web-based applications by utilizing the Delphi methodology.