The 7th International Conference on Data Mining 2011 - DMIN'11.
Research Paper Adverse Event Profiles of Platinum Agents: Data Mining of the Public Ver-sion of the FDA Adverse Event Reporting System, AERS, and Reproducibility of Clinical Observations Toshiyuki Sakaeda 2,1, Kaori 1Kadoyama, and Yasushi Okuno 3 1. Center for Integrative Education in Pharmacy and Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto.
Data mining has made a great progress in recent year but the problem of missing data has remained a great challenge for data mining algorithms. It is an activity of extracting some useful knowledge from a large data base, by using any of its techniques.Data mining is used to discover knowledge out of data and presenting it in a form that is easily understood to humans.
Abstract Mining valuable patterns in different data streams have been a significant research area in data mining research during the last decade. There are several proposed techniques for data mining that have been developed for mining patterns from different text documents. But to determine the method in which the patterns are discovered effectively is a popular issue in data mining research.
Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoe ding trees and Naive Bayes.
We present our early explorations into developing a data mining based approach for enhancing the quality of textbooks. We describe a diagnostic tool to algorithmically identify deficient sections in textbooks. We also discuss techniques for algorithmically augmenting textbook sections with links to selective content mined from the Web. Our evaluation, employing widely-used textbooks from India.
Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper, we explore the emerging field of spatial data mining, focusing on.
Crowdsourcing Data in Mining Spatial Urban Activities - Dr Elisabette Silva; IOT Network Behaviours and Dependencies - Dr Richard Mortier; Visualising the Future: Big Data and the Built Environment - Prof Paul Linden; 2018 Research Networks. 2018 Research Networks overview; Network - Housing Digital Built Britain; Network - D-COM; Network - FOuNTAIN; Network - Vision; Network - Methodologies.