Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

A new measure to quantify chest wall deformity in scoliosis patients : does rib hump measurement correlate with anterior chest wall deformity post surgery?

The primary aims of scoliosis surgery are to halt the progression of the deformity, and to reduce its severity (cosmesis). Currently, deformity correction is measured in terms of posterior parameters (Cobb angles and rib hump), even though the cosmetic concern for most patients is anterior chest wall deformity. In this study, we propose a new measure for assessing anterior chest wall deformity and examine the correlation between rib hump and the new measure. 22 sets of CT scans were retrieved from the QUT/Mater Paediatric Spinal Research Database. The Image J software (NIH) was used to manipulate formatted CT scans into 3-dimensional anterior chest wall reconstructions. A ‘chest wall angle’ was then measured in relation to the first sacral vertebral body. The chest wall angle was found to be a reliable tool in the analysis of chest wall deformity. No correlation was found between the new measure and rib hump angle. Since rib hump has been shown to correlate with vertebral rotation on CT, this suggests that there maybe no correlation between anterior and posterior deformity measures. While most surgical procedures will adequately address the coronal imbalance & posterior rib hump elements of scoliosis, they do not reliably alter the anterior chest wall shape. This implies that anterior chest wall deformity is to a large degree an intrinsic deformity, not directly related to vertebral rotation.