1000 resultados para Frequent mining


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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.

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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.

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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.

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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.

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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.

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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.