By Michael R. Berthold (auth.), João Gama, Elizabeth Bradley, Jaakko Hollmén (eds.)
This publication constitutes the refereed court cases of the tenth overseas convention on clever facts research, IDA 2011, held in Porto, Portugal, in October 2011. the nineteen revised complete papers and sixteen revised poster papers resented including three invited papers have been rigorously reviewed and chosen from seventy three submissions. All present elements of clever information research are addressed, rather clever aid for modeling and studying advanced, dynamical structures. The papers supply clever help for figuring out evolving medical and social platforms together with facts assortment and acquisition, resembling crowd sourcing; info cleansing, semantics and markup; looking for facts and assembling datasets from a number of resources; facts processing, together with workflows, mixed-initiative information research, and making plans; information and knowledge fusion; incremental, mixed-initiative version improvement, trying out and revision; and visualization and dissemination of effects; etc.
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Extra resources for Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings
Context awareness is an important part of ubiquitous computing [17,5,1]. In most ubiquitous applications concepts are associated with context, this means that they may reappear when a similar context occurs. For example, a weather prediction model usually changes according to the seasons. The same applies with product recommendations or text ﬁltering models where the user interest and behaviour might change over time due to fashion, economy, spatial-temporal situation or any other context [25,9,20].
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The number of predictions diﬀerent from fd ). However, the underlying concept of interest fd may change over time and the number of labelled records available for that concept are sometimes limited. To address such situations, we propose to exploit models from the community and use the available labelled records from DSd to obtain a model md . We expect md to be more accurate than using the local labelled records alone when building the model. The incremental learning of md should adapt to changes in the underlying concept.