Advances in Intelligent Data Analysis X: 10th International by Michael R. Berthold (auth.), João Gama, Elizabeth Bradley, PDF

By Michael R. Berthold (auth.), João Gama, Elizabeth Bradley, Jaakko Hollmén (eds.)

ISBN-10: 3642247997

ISBN-13: 9783642247996

ISBN-10: 3642248004

ISBN-13: 9783642248009

This booklet constitutes the refereed complaints of the tenth foreign convention on clever information 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 facets of clever information research are addressed, relatively clever aid for modeling and examining advanced, dynamical platforms. The papers provide clever aid for knowing evolving clinical and social structures together with information assortment and acquisition, corresponding to crowd sourcing; info cleansing, semantics and markup; trying to find information and assembling datasets from a number of resources; info processing, together with workflows, mixed-initiative facts research, and making plans; information and knowledge fusion; incremental, mixed-initiative version improvement, checking out and revision; and visualization and dissemination of effects; etc.

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Extra info for Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings

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On one hand, text mining methods treat each document as an independent instance despite the fact that in many text domains, documents are linked and their topics are correlated. For example, web pages of related topics are often connected by hyperlinks and scientific papers from related fields are typically linked by citations. On the other hand, Social Network Analysis (SNA) typically treats edges between nodes according to ”flat” attributes in binary form alone. This paper proposes a simple approach that addresses both these issues in data mining scenarios involving corpora of linked documents.

Conditioned similarity matrix : this matrix contains the L1 distances for each one (or all) of the electrical components, conditioned on the environmental variables, so that different matrices can be obtained for different conditions and thresholds within each condition. For instance, it could be interesting to analyze power consumption when temperature is high or low, different time periods (morning, afternoon and night), etc. The matrix is computed using k = L1 (B k (p), B k (q))|v = CSpq i (mpik − mqik ) / ν1 < v < ν2 , (5) where v is the environmental variable and ν1 and ν2 are the lower and upper threshold values for desired range.

Travail Humain 62, 223–246 (1999) 2. : Symbiotic Data Mining for Personalized Spam Filtering. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT 2009, vol. 1, pp. 149–156. IEEE, Los Alamitos (2009) 32 J. B´ artolo Gomes et al. 3. : Distributed data mining in peer-to-peer networks. IEEE Internet Computing, 18–26 (2006) 4. : Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26(1), 101–126 (2006) 5.

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Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings by Michael R. Berthold (auth.), João Gama, Elizabeth Bradley, Jaakko Hollmén (eds.)


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