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

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 may well 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; category; desktop studying; functions; novel tools and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and vague facts; mining temporal and spatial information; function extraction and choice; mining heterogeneous, high-dimensional and sequential facts; entity answer and topic-modeling; itemset and high-performance info mining; and recommendations.

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Read or Download Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I PDF

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Additional info for Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I

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Fig. 1. Illustrative Example indicates that individuals u and v are likely to become friends (the edge weight w[u , v ] quantifies the likelihood). The potential edges and the corresponding edge weights can be obtained by employing a link prediction algorithm in friend recommendation. , too small a group may not work well for socialization activities. Figure 1 illustrates the social graph and the interplay of the above factors. , (c, d), is a friend edge. Figure 1(b) shows a group H1 :{a, e, f, g} which has many potential edges and thus a high total weight.

Saurabh Gupta, Sayan Pathak, and Bivas Mitra 720 Coupling Multiple Views of Relations for Recommendation . . . . . . Bin Fu, Guandong Xu, Longbing Cao, Zhihai Wang, and Zhiang Wu 732 Pairwise One Class Recommendation Algorithm . . . . . . . . . . Huimin Qiu, Chunhong Zhang, and Jiansong Miao 744 RIT: Enhancing Recommendation with Inferred Trust. . . . . . . . . Guo Yan, Yuan Yao, Feng Xu, and Jian Lu 756 Author Index . . . . . . . . . . . .

Xia et al. method. The benefit of this method is that, we can extract different features from Twitter and Instagram, and further incorporate other inhomogeneous data sources. Feature Extraction. To represent an event signal e(l, t), we extract four types of features from all the posts bounded by l and t, namely topic features, emotional features, spatial features and social features. , pn } to denote the set of posts associated with the event e(l, t) and n = |Pe |. Note that, here we do not extract feature from a single post, instead, we extract features from the set of posts Pe associated to event signal e(l, t).

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