A border-based approach for hiding sensitive frequent itemsets

Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach. © 2005 IEEE

Virtual teachers: Negotiating new spaces for teaching bodies

In 2002, the authors reviewed the educational performance of a state education department virtual schooling service during its first 2 years of operation, 2000-2001 (Pendergast, Kapitzke, Land, Luke, & Bahr, 2002). Established by Education Queensland, the Virtual Schooling Service (VSS) utilises synchronous and asynchronous online delivery strategies and a range of learning technologies to support students at a distance (see http://education.qld.gov.au/learningplace/vss/). The service commenced with a focus on senior secondary subjects. At present, there are over 700 students in 89 schools across the state enrolled in 9 subjects. In response to the recommendations of the study, a series of professional development activities were conducted with the VSS teachers by the authors. Opportunity for critical reflection was provided, including consideration of the ways in which the teachers were developing as a learning community. Some data, including visual representations, were collected from participants with the purpose of understanding how VSS teachers are constructed as professionals. This study compares and contrasts that data with self-constructions of teacher professionals in other fields.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.