Advances in Knowledge Discovery and Data Mining: 19th by Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung,

By Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung, Hiroshi Motoda

This two-volume set, LNAI 9077 + 9078, constitutes the refereed court cases of the nineteenth Pacific-Asia convention on Advances in wisdom Discovery and knowledge Mining, PAKDD 2015, held in Ho Chi Minh urban, Vietnam, in could 2015.

The lawsuits include 117 paper conscientiously reviewed and chosen from 405 submissions. they've been geared up in topical sections named: social networks and social media; type; laptop studying; functions; novel equipment and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and obscure facts; mining temporal and spatial info; characteristic extraction and choice; mining heterogeneous, high-dimensional and sequential information; entity solution and topic-modeling; itemset and high-performance info mining; and recommendations.

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Edu Abstract. The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, inperson interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making.

In: KDD (2012) 7. : Geo-Social Group Queries with Minimum Acquaintance Constraint (2014). 7367v1 8. : Willingness Optimization for Social Group Activity. VLDB (2014) 9. : Recommending people in developers’ collaboration network. In: WCRE (2011) Maximizing Friend-Making Likelihood for Social Activity Organization 15 10. , Abe, N: A parameterized probabilistic model of network evolution for supervised link prediction. In: ICDM (2006) 11. : The Link Prediction Problem for Social Networks. Journal of the American Soceity for Information Science and Technology (2007) 12.

1. 1 Event Signal Discovery The Event Signal Discovery component contains 3 sub-components, data stream collector, time series estimator and bursty detector. The motivation behind follows the general idea of modeling bursts [16] of certain features as potential events. Unlike other papers which model the sudden change of emotions [32], the movements of crowds [20] or the trending topics/terms/n-grams [21][33][7], we adopt the method in [34][35] which considers the abnormal increase of social media posts as the potential signal of events.

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