Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

The quality of referendum debate: the UK's electoral system referendum in the print media

This paper analyses the quality of debate surrounding the UK’s 2011 electoral reform referendum as represented in the print media. It first considers how debate quality in the context of a referendum campaign may best be conceptualized. It then uses content analysis of media coverage to investigate three aspects of that debate: its quantity; the balance between Yes and No arguments, and the quality of reason-giving. It finds that the quantity of debate was comparable to other recent electoral reform referendums. Coverage was predominantly, but not overwhelmingly, hostile to change. The different indicators of the quality of reason-giving present a mixed picture. The paper concludes by considering how the analysis could be extended through further comparison with other cases.