Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Triple objective team mentoring: achieving learning objectives with chemical engineering students

A sophisticated style of mentoring has been found to be essential to support engineering student teams undertaking technically demanding, real-world problems as part of a Project-Centred Curriculum (PCC) at The University of Queensland. The term ‘triple-objective’ mentoring was coined to define mentoring that addresses not only the student’s technical goal achievement but also their time and team management. This is achieved through a number of formal mentor meetings that are informed by a confidential instrument which requires students to individually reflect on team processes prior to the meeting, and a checklist of technical requirements against which the interim student team progress and achievements are assessed. Triple-objective mentoring requires significant time input and coordination by the academic but has been shown to ensure effective student team work and learning undiminished by team dysfunction. Student feedback shows they value the process and agree that the tools developed to support the process are effective in developing and assessing team work and skills with average scores mostly above 3 on a four point scale.

The Learning Tourism Destination: The potential of a learning organisation approach for improving the sustainability of tourism destinations

Globalisation, increasing complexity, and the need to address triple-bottom line sustainability has seen the proliferation of Learning Organisations (LO) who, by definition, have the capacity to anticipate environmental changes and economic opportunities and adapt accordingly. Such organisations use system dynamics modelling (SDM) for both strategic planning and the promotion of organisational learning. Although SDM has been applied in the context of tourism destination management for predictive reasons, the current literature does not analyse or recognise how this could be used as a foundation for an LO. This study introduces the concept of the Learning Tourism Destinations (LTD) and discusses, on the basis of a review of 6 case studies, the potential of SDM as a tool for the implementation and enhancement of collective learning processes. The results reveal that SDM is capable of promoting communication between stakeholders and stimulating organisational learning. It is suggested that the LTD approach be further utilised and explored.

A shift from passive teaching at medical conferences to more interactive methods improves physician learning

Conferences that deliver interactive sessions designed to enhance physician participation, such as role play, small discussion groups, workshops, hands-on training, problem- or case-based learning and individualised training sessions, are effective for physician education.

Language policy spread: Learning from health and social policy models

Except for a few large scale projects, language planners have tended to talk and argue among themselves rather than to see language policy development as an inherently political process. A comparison with a social policy example, taken from the United States, suggests that it is important to understand the problem and to develop solutions in the context of the political process, as this is where decisions will ultimately be made.

Dynamic processes in contingent valuation: A case study involving the mahogany glider

This paper reports the results of an experiment involving a sample of 204 members of the public who were assessed on three occasions about their willingness to pay for the conservation of the mahogany glider. They were asked this question prior to information being provided to them about the glider and other focal wildlife species; after such information was provided, and finally after participants had had an opportunity to see live specimens of this glider. The mean willingness to pay of the relevant samples are compared and found to show significant variations. Theories are considered that help explain the dynamics of these variations. Serious concerns are raised about the capacity of information provision to reveal ‘true’ contingent valuations of public goods.