Mining

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

By Longbing Cao

This publication constitutes the completely refereed and revised chosen papers from the ninth overseas Workshop on brokers and information Mining interplay, ADMI 2013, held in Saint Paul, MN, united states in may possibly 2013. the ten papers provided during this quantity have been rigorously chosen for inclusion within the publication and are prepared in topical sections named agent mining and knowledge mining.

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

Example text

Second, it presents an architecture for mining norms from open source projects. It also discusses the opportunities presented by the domain of software repositories for the study of norms. In particular, it points towards how norms can be mined by leveraging and extending prior work in the areas of Normative Multi-Agent Systems (NorMAS) and mining software repositories. 1 Introduction A good example of a large, real-world multi-agent organization involving a huge number of (human) agents is open source software development (OSSD).

15,16] can be used as a starting point to infer prohibition and obligation norms. In their work, prohibition norms are identified by extracting sequence of action (or actions) that could have caused the sanction by using a data mining approach [16]. Sanctions form the starting point for norm identification. In the case of obligation norms, missing event sequence (or sequences) that was responsible for the occurrence of a sanction, is identified [15]. e. the reason for the sanction is the violation of a prohibition or an obligation norm).

7. 5 Contrast Experiment by Setting Different Time Slice Size In order to understand the importance of the time slice size, we replace the TSS (time slice size) and set for a month, three months and six months, and test the model on different the KL threshold value. The results are shown in Fig. 7. As can be seen, TSS and KL threshold affect the overall performance. Therefore, how to adjust the model automatically and set a suitable time slice size will be focused on future research. It is interesting to see that above experiments can identify the target group {2, 4} and {2, 7} accurately, but does not identify the {4, 7}, which is mainly due to the reason of the data set.

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