Prediction of paediatric asthma hospitalisation using data mining techniques

Research into the prevalence of hospitalisation among childhood asthma cases is undertaken, using a data set local to the Barwon region of Victoria. Participants were the parents/guardians on behalf of children aged between 5-11 years. Various data mining techniques are used, including segmentation, association and classification to assist in predicting and exploring the instances of childhood hospitalisation due to asthma. Results from this study indicate that children in inner city and metropolitan areas may overutilise emergency department services. In addition, this study found that the prediction of hospitalisaion for asthma in children was greater for those with a written asthma management plan.

Magnum Opus

Magnum Opus is commercial association discovery software that implements many discovery techniques. Magnum Opus is a highly flexible tool that can be used for many forms of data mining analysis. For example, it can be used for contrast discovery (also known as emerging pattern discovery and closely related to subgroup discovery).

Ornament colour selection, visual contrast and the shape of colour preference functions in great bowerbirds, Chlamydera nuchalis

A male bowerbird visual signal includes his own plumage, a structure he  constructs out of plant material and coloured objects (ornaments) he places on or near the structure to make up the bower. Plumage and bower together are used to attract females for mating. Ornaments are known to contrast with plumage, bower structure and visual backgrounds in seven Australian bowerbird species (Endler et al. 2005, Evolution, 50, 1795-1818). We estimated the colour preferences in a wild population of great bowerbirds using artificially coloured objects widely spaced in bird colour space. We found that these birds prefer colours that contrast with their own plumage, the bower structure and the visual backgrounds adjacent to the bower, and that they have very strong dislikes for colours that are similar to their own plumage and to the visual backgrounds. The range of disliked colour hues was much narrower than the range of preferred hues, suggesting that the word 'preference' may be misleading. Preferences for colour are inherently multidimensional and should be studied in the context of their function.

Application of hermeneutics to studying an experience mining process

In this paper we investigate an approach to eliciting practitioners’ problem-solving experience across an application domain. The approach is based on a well-known ‘pattern mining’ process which commonly results in a collection of sharable and reusable ‘design patterns’. While pattern mining has been recognised to work effectively in numerous domains, its main problem is the degree of technical proficiency that few domain practitioners are prepared to master. In our approach to pattern mining, patterns are induced indirectly from designers’ experience, as determined by analysing their past projects, the problems encountered and solutions applied in problem rectification. Through the cycles of hermeneutic revisions, the pattern mining process has been refined and ultimately its deficiencies addressed. The hermeneutic method used in the study has been clearly shown in the paper and illustrated with examples drawn from the multimedia domain. The resulting approach to experience elicitation provided opportunities for active participation of multimedia practitioners in capturing and sharing their design experience.

Reducing performance bias by intrinsically insensitive learning for unbalanced text mining

This thesis proposes three effective strategies to solve the significant performance-bias problem in imbalance text mining: (1) creation of a novel inexact field learning algorithm to overcome the dual-imbalance problem; (2) introduction of the one-class classification-framework to optimize classifier-parameters, and (3) proposal of a maximal-frequent-item-set discovery approach to achieve higher accuracy and efficiency.

Mining frequent itemsets in distorted databases with granula computing

Data perturbation is a popular method to achieve privacy-preserving data mining. However, distorted databases bring enormous overheads to mining algorithms as compared to original databases. In this paper, we present the GrC-FIM algorithm to address the efficiency problem in mining frequent itemsets from distorted databases. Two measures are introduced to overcome the weakness in existing work: firstly, the concept of independent granule is introduced, and granule inference is used to distinguish between non-independent itemsets and independent itemsets. We further prove that the support counts of non-independent itemsets can be directly derived from subitemsets, so that the error-prone reconstruction process can be avoided. This could improve the efficiency of the algorithm, and bring more accurate results; secondly, through the granular-bitmap representation, the support counts can be calculated in an efficient way. The empirical results on representative synthetic and real-world databases indicate that the proposed GrC-FIM algorithm outperforms the popular EMASK algorithm in both the efficiency and the support count reconstruction accuracy.

Multiagent framework for bio-data mining

This paper proposes to apply multiagent based data mining technologies to biological data analysis. The rationale is justified from multiple perspectives with an emphasis on biological context. Followed by that, an initial multiagent based bio-data mining framework is presented. Based on the framework, we developed a prototype system to demonstrate how it helps the biologists to perform a comprehensive mining task for answering biological questions. The system offers a new way to reuse biological datasets and available data mining algorithms with ease.