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

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

ISBN-10: 3319180371

ISBN-13: 9783319180373

ISBN-10: 331918038X

ISBN-13: 9783319180380

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

The court cases include 117 paper conscientiously reviewed and chosen from 405 submissions. they've been geared up in topical sections named: social networks and social media; class; desktop studying; purposes; novel tools and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and vague information; mining temporal and spatial information; function extraction and choice; mining heterogeneous, high-dimensional and sequential facts; entity solution and topic-modeling; itemset and high-performance info mining; and recommendations.

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Read Online 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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User(h=2) MaxGF(h=2) User_FeaRatio MaxGF_FeaRatio 100% User(h=3) MaxGF(h=3) 600 Ratio Time (s) 800 400 200 User_ObjRatio MaxGF_ObjRatio 80% 80% 60% 10 14 18 10 22 |V| (p=2) (a) Required Time. 60% 40% 20% 40% 0 User Satisfaction 100% Ratio 12 14 18 |V| (h=2, p=2) 22 (b) FeaRatio and ObjRatio. 0% User DkS MaxGF (c) User Satisfaction. Fig. 2. User Study Results of solutions satisfying the hop constraint) and the ratio of σ(H) in the solutions obtained by MaxGF or DkS to that of the optimal solution.

We can either extract features from Instagram posts or Twitter posts only, or from both of them. In this paper, we consider two methods to fuse two data sources for feature extraction and classification. e. before feature extraction. In this way, we need to consider a Twitter post and a Instagram post as homogeneous. For each event signal e(l, t), we extract its features vector xe from all the posts during time period t within location l. This method mitigates the sparsity problem of geo-tagged posts, and it is expected to benefit the classification of small-scale events with a few of posts in total.

We finally extract 28 features of four categories in total. We also normalize the topic and emotional features by text length. What Is New in Our City? A Framework for Event Extraction 23 Fig. 2. Five sampled Instagram photos from a detected Knicks NBA game event in NYC. From journalists’ perspective, the first three images are considered representative to summarize the event. Although the last two images were uploaded at the stadium and their captions are also about game, they are not informative for describing this event.

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


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