This paper studies the problem of frequent pattern mining with uncertain data. We will show how broad classes of algorithms can be extended to the uncertain data setting. In particular, we will study candidate generate-and-test algorithms, hyper-structure algorithms and pattern growth based algorithms. One of our insightful observations is that the experimental behavior of different classes of algorithms is very different in the uncertain case as compared to the deterministic case. In particular, the hyper-structure and the candidate generate-and-test algorithms perform much better than tree-based algorithms. This counter-intuitive behavior is an important observation from the perspective of algorithm design of the uncertain variation of the problem. We will test the approach on a number of real and synthetic data sets, and show the effectiveness of two of our approaches over competitive techniques.
Literature review has found that despite the considerable attention focused on 'digital natives', few studies have carefully investigated the characteristics of this group. The purpose of this study is to contribute to the debate on digital natives by providing a 'piece of evidence' on the digital competence status of a group of Chinese teenagers (ninth grade students) randomly selected from the Jiangdong District in Ningbo City, Zhejiang Province. An Instant Digital Competence Assessment (iDCA) tool, developed by a research group from the University of Florence, was adopted as the measurement tool for the study. Quantitative research was employed and the research design for the study was descriptive in nature. Data analysis results found that the majority of the participating ninth grade students (n = 317) had personal computers (PCs) and the Internet available at home and the average period of time owing a PC was about 5 years. The iDCA results indicated that (1) participants' overall performance in the iDCA was just 'pass' rather than 'good' or 'excellent', which might imply that digital natives in China are not necessarily digitally competent; (2) there were big disparities among participants as regards their digital competence; (3) participants' digital competence differed depending on their schools and their ages; (4) participants' digital competence was not significantly influenced by such factors as having a PC or not, having the Internet or not at home, frequency of computers and Internet use. On the basis of the findings, the study concluded by highlighting the role of education in improving teenagers' digital competence and by recommending the development of well-designed teaching and learning materials for the Chinese K-12 school system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.