Download Advances in Knowledge Discovery and Data Mining: 18th by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, PDF

By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2014, held in Tainan, Taiwan, in may perhaps 2014. The forty complete papers and the 60 brief papers provided inside those complaints have been rigorously reviewed and chosen from 371 submissions. They disguise the final fields of development mining; social community and social media; type; graph and community mining; functions; privateness conserving; suggestion; function choice and aid; computing device studying; temporal and spatial info; novel algorithms; clustering; biomedical facts mining; circulation mining; outlier and anomaly detection; multi-sources mining; and unstructured information and textual content mining.

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Read or Download Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II PDF

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287–296. ACM, New York (2011) 11. : Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 1267–1275. ACM, New York (2012) 12. : A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22(1-2), 31–72 (2011) 13. : Trust based recommender system for semantic web. In: IJCAI, pp. tw Abstract. This paper presents a novel framework for dynamic circle recommendation for a query user at a given time point from historical communication logs.

A) MAE for LLR1 (b) MAE for LLR2 (c) RMSE for LLR1 (d) RMSE for LLR2 Fig. 3. The results of different phases of LLR1 and LLR2 (Dimensionality = 5) The Item Content and Social Networks Information: After we get the baseline estimate, we discuss how the content and social information may contribute to improving the values. 2702 on MAE/RMSE respectively. 46% in contrast to phase one. 35%. The improvement demonstrates that the content and social information are helpful to boost the performance, especially for LLR1, although classification on the flat approach improves much less than LLR2 based on the top-down approach in phase one, the information significantly enhance more accuracy than LLR2 in phase two.

25–37, 2014. -K. Chou et al. Fig. 1. Distribution of Time Centrality that the notion of group should dynamically adapt to context information such as location, time, etc. That is, some users may have the tendency to share information to different groups of friends at certain time points while some users may share information to the same group of friends at all time. For example, a user may have the tendency to share information to his/her family during daytime and share information to his/her close colleagues in the evening.

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