Get 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 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 complaints comprise 117 paper rigorously reviewed and chosen from 405 submissions. they've been equipped in topical sections named: social networks and social media; category; laptop studying; purposes; novel tools and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and obscure facts; mining temporal and spatial info; function extraction and choice; mining heterogeneous, high-dimensional and sequential information; entity answer and topic-modeling; itemset and high-performance information 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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Other than detecting the trending topics, based on influential theories of emotions, [32] automatically assigns a single tweet with an emotional label which is neutral or comes from one of the 6 Ekman’s emotions. Then they monitor the sudden change of tweets’ emotions in countries as the signals to detect events. All of the above works consider burst of certain features as signals of potential events. They model different bursty features including n-gram, terms, topics and emotions, and the common idea behind is absorbed into our framework.

According to [3][5], the text relevance of a photo x to the event e could be computed by the closeness of x to the centroid ct . Thus, we compute st (x, e) as the cosine similarity between x and ct . Geolocation Relevance Model. Each photo is associated with a coordinate (u, v) which denotes its latitude and longitude respectively. Similarly, we comn n pute the geographical centroid cl of the event as ( n1 i=1 ui , n1 i=1 vi ) where ui and vi respectively denote the latitude and longitude of the i-th photo of the event.

Shen et al. 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.

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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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