Safely delegating data mining tasks

Data mining is playing an important role in decision making for business activities and governmental administration. Since many organizations or their divisions do not possess the in-house expertise and infrastructure for data mining, it is beneficial to delegate data mining tasks to external service providers. However, the organizations or divisions may lose of private information during the delegating process. In this paper, we present a Bloom filter based solution to enable organizations or their divisions to delegate the tasks of mining association rules while protecting data privacy. Our approach can achieve high precision in data mining by only trading-off storage requirements, instead of by trading-off the level of privacy preserving.

The underpricing of gold mining initial public offerings

This study is motivated by How [How, J., 2000. The initial and long run performances of mining IPOs in Australia. Aust. J. Manage. 25, 95–118] who examined 100 Australian gold mining initial public offerings (IPOs) from 1979 to 1990 to report an average 119.51% underpricing return by those IPOs. This study updates that analysis by investigating 114 Australian gold mining IPOs from 1994 to 2004 and finds a significantly lower 13.3% average first day return. Options offered to underwriters can in part explain these returns as can the change in either the Gold Index or the All Ordinaries Index from the date of the prospectus to the date of listing.

Total arsenic accumulation in yabbies (Cherax Destructor Clark) exposed to elevated arsenic levels in Victorian gold mining areas, Australia

Arsenic is a proven carcinogen often found at high concentrations in association with gold and other heavy metals. The freshwater yabby, Cherax destructor Clark (Decapoda, Parastacidae), is a ubiquitous species native to Australia's central and eastern regions, with a growing international commercial market. However, in this region of Australia, yabby farmers often harvest organisms from old mine tailings dams with elevated environmental arsenic levels. Yabbies exposed to elevated environmental arsenic were found to accumulate and store as much as 100 μg/g arsenic in their tissues. The accumulation is proportional to the concentration of arsenic in the sediment and is high enough to be of concern for people who eat the yabbies. A comparison of arsenic levels in wild and lab-fed animals also was performed. Although there was no significant difference in the level of arsenic in the various organs of the wild animals, the animals purchased from a yabby farm showed a significantly higher arsenic concentration in their hepatopancreas (3.7 ± 0.9 μg/g) compared to other organs (0.6–1.8 μg/g). Furthermore, after a 40-d exposure to food containing 200 to 300 μg/g inorganic arsenic, arsenate (As[V])-exposed animals showed a significant increase in tissue-specific arsenic accumulation, whereas arsenite (As[III])-exposed animals showed a lower, nonsignificant increase in As uptake, primarily in the hepatopancreas. These results have important implications for yabby growers and consumers alike.

Online mining of frequent sets in data streams with error guarantee

For most data stream applications, the volume of data is too huge to be stored in permanent devices or to be thoroughly scanned more than once. It is hence recognized that approximate answers are usually sufficient, where a good approximation obtained in a timely manner is often better than the exact answer that is delayed beyond the window of opportunity. Unfortunately, this is not the case for mining frequent patterns over data streams where algorithms capable of online processing data streams do not conform strictly to a precise error guarantee. Since the quality of approximate answers is as important as their timely delivery, it is necessary to design algorithms to meet both criteria at the same time. In this paper, we propose an algorithm that allows online processing of streaming data and yet guaranteeing the support error of frequent patterns strictly within a user-specified threshold. Our theoretical and experimental studies show that our algorithm is an effective and reliable method for finding frequent sets in data stream environments when both constraints need to be satisfied.

Utility of real-time decision-making in commercial data stream mining domains

The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual utility with a monetary value instead of a prediction. The second is to calculate the benefit from predictions using the utility-based decision threshold. The third is to incorporate cost of predictions. For experiments, overall utility is used to evaluate communal and spike detection, and their adaptive versions. The overall utility results show that with fewer alerts, communal detection is better than spike detection. With more alerts, adaptive communal and spike detection are better than their static versions. To maximise overall utility with all algorithms, only 1% to 4% in the highest predictions should be alerts.

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.

Associations of maternal long-chain polyunsaturated fatty acids, methyl mercury, and infant development in the Seychelles Child Development Nutrition Study

Fish consumption during gestation can provide the fetus with long-chain polyunsaturated fatty acids (LCPUFA) and other nutrients essential for growth and development of the brain. However, fish consumption also exposes the fetus to the neurotoxicant, methyl mercury (MeHg). We studied the association between these fetal exposures and early child development in the Seychelles Child Development Nutrition Study (SCDNS). Specifically, we examined a priori models of Ω-3 and Ω-6 LCPUFA measures in maternal serum to test the hypothesis that these LCPUFA families before or after adjusting for prenatal MeHg exposure would reveal associations with child development assessed by the BSID-II at ages 9 and 30 months. There were 229 children with complete outcome and covariate data available for analysis. At 9 months, the PDI was positively associated with total Ω-3 LCPUFA and negatively associated with the ratio of Ω-6/Ω-3 LCPUFA. These associations were stronger in models adjusted for prenatal MeHg exposure. Secondary models suggested that the MeHg effect at 9 months varied by the ratio of Ω-6/Ω-3 LCPUFA. There were no significant associations between LCPUFA measures and the PDI at 30 months. There were significant adverse associations, however, between prenatal MeHg and the 30-month PDI when the LCPUFA measures were included in the regression analysis. The BSID-II mental developmental index (MDI) was not associated with any exposure variable. These data support the potential importance to child development of prenatal availability of Ω-3 LCPUFA present in fish and of LCPUFA in the overall diet. Furthermore, they indicate that the beneficial effects of LCPUFA can obscure the determination of adverse effects of prenatal MeHg exposure in longitudinal observational studies.