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Read e-book online Agents and Data Mining Interaction: 8th International PDF

By Victor Lesser (auth.), Longbing Cao, Yifeng Zeng, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Philip S. Yu, Munindar P Singh (eds.)

ISBN-10: 3642362877

ISBN-13: 9783642362873

ISBN-10: 3642362885

ISBN-13: 9783642362880

This ebook constitutes the completely refereed post-workshop lawsuits of the eighth foreign Workshop on brokers and information Mining interplay, ADMI 2012, held in Valencia, Spain, in June 2012. The sixteen revised complete papers have been conscientiously reviewed and chosen from a number of submissions. The papers are prepared in topical sections on brokers for facts mining, info mining for brokers, and agent mining applications.

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Read Online or Download Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers PDF

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Additional resources for Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers

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G. Matlab [12] or R Project [22] with focus on unified environment and computational efficiency. However, these frameworks are closed, they are inflexible in replacement of methods and they do not allow automated construction of data-mining processes. Moreover, it impossible to utilize their methods in various contexts such as meta-learning or recommending methods and settings based on previous experience. Other systems, such as the KNIME environment [4] focus on smooth user-friendly interface to complex hybrid models by offering visual assembly of data-mining processes.

Possible simulation scenarios of indirect interaction cycle of length two is possi- the Indirect Interaction Mechanism ble. Multi-swarming peers withhold the contents of their second file from other leechers. They also disconnect as soon as they have finished downloading the file. All peers have the same upload speed (homogeneous environment). The results have been obtained with 50 simulation runs. These simulations are mirrored with the exact same number of runs in which the multi-swarmers do not interact indirectly (act as standard leechers).

G. in [9]. We present a rolebased model of complex data-mining scenarios, such as meta-learning, parallel computing, ensemble methods or pre-processing of data. The no-free-lunch theorem [24] shows that there is no best approach for every task. In practice the user does not know which method to use and how to set its parameters. Agent-based solution enables automatic assembly of the system. The previous experience can be stored for later experiments on similar data. In the next section, we present a computational intelligence scenario, perform its analysis, and elaborate the role-based model of a computational MAS.

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Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers by Victor Lesser (auth.), Longbing Cao, Yifeng Zeng, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Philip S. Yu, Munindar P Singh (eds.)


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