Agents and Data Mining Interaction: 9th International by Longbing Cao

By Longbing Cao

This booklet constitutes the completely refereed and revised chosen papers from the ninth foreign Workshop on brokers and knowledge Mining interplay, ADMI 2013, held in Saint Paul, MN, united states in could 2013. the ten papers awarded during this quantity have been conscientiously chosen for inclusion within the ebook and are prepared in topical sections named agent mining and information mining.

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Agents and Data Mining Interaction: 9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers

This ebook constitutes the completely refereed and revised chosen papers from the ninth overseas Workshop on brokers and knowledge Mining interplay, ADMI 2013, held in Saint Paul, MN, united states in may well 2013. the ten papers offered during this quantity have been conscientiously chosen for inclusion within the e-book and are geared up in topical sections named agent mining and information mining.

Extra resources for Agents and Data Mining Interaction: 9th International Workshop, ADMI 2013, Saint Paul, MN, USA, May 6-7, 2013, Revised Selected Papers

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4 Contrast with Author Topic Model Under the default parameters, we change the KL distance threshold and compare the results between MVIA-MAS and Author Topic model. We use the F1 value to evaluate the experimental results. F1 = 2 · precision · recall precision + recall (2) F1 value is a measure of a test’s accuracy. It considers both the precision p and the recall r of the test to compute the score. The results are shown in Fig. 6. As can be seen, in the case of replacement of the KL threshold value, MVIAMAS uses time factor to analysis the dataset in the preprocessing and use association rule mining to filter the accidental events, which performs better than the AT model method, the ability to identify is better and relatively stable.

6. 4 Contrast with Author Topic Model Under the default parameters, we change the KL distance threshold and compare the results between MVIA-MAS and Author Topic model. We use the F1 value to evaluate the experimental results. F1 = 2 · precision · recall precision + recall (2) F1 value is a measure of a test’s accuracy. It considers both the precision p and the recall r of the test to compute the score. The results are shown in Fig. 6. As can be seen, in the case of replacement of the KL threshold value, MVIAMAS uses time factor to analysis the dataset in the preprocessing and use association rule mining to filter the accidental events, which performs better than the AT model method, the ability to identify is better and relatively stable.

There are three types of agents, sanction monitoring agent, reasoning agent and decision-making agent. g. code repository and bug repository) which are mined for appropriate information. The process associated with identifying two types of norms, the pre-specified norms and the emergent norms is described below. g. messages containing sanctions) that are added to any of the monitored repositories. For example, a message may contain information where agent A informs B that it has violated a norm.

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