Read e-book online 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: 3319180312

ISBN-13: 9783319180311

ISBN-10: 3319180320

ISBN-13: 9783319180328

This two-volume set, LNAI 9077 + 9078, constitutes the refereed lawsuits 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 possibly 2015.

The lawsuits comprise 117 paper conscientiously reviewed and chosen from 405 submissions. they've been prepared in topical sections named: social networks and social media; category; desktop studying; functions; novel equipment and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and vague facts; mining temporal and spatial info; function 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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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 II PDF

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Extra 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 II

Example text

Com/svn/trunk/ 8 K. Gao et al. within tn at the same time, the corresponding co-occurrence frequency is called |CO(ci , rkj )|. As for the co-occurrence intensity between the corresponding emoticon and the emotion keyword, it can be represented as δij (ci , rkj ), see the formula(2), where |ci | is the number of ci appearing in tn , and |rkj | is the number of rkj appearing in tn . δij (ci , rkj ) = |CO(ci , rkj )| (|ci | + |rkj |) − |CO(ci , rkj )| (2) According to the above definitions, it is easy to construct the co-occurrence graph (see Figure 1).

P (Emom |Caun ) = P (Caun |Emom )P (Emom ) 22 m=1 P (Emom )P (Caun |Emom ) (9) In the above formula (9), the parameter Emom is the mth emotion and Caun is the nth cause component under Emom . The prior probability P (Emom ) is the probability distribution of Emom . It can be calculated by the formula (10) and (11), Where SCORE(Emom ) is the mth emotion intensity score which can Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model 11 be modified by the multiple features in micro-blogs.

Jan Kralj, Anita Valmarska, Marko Robnik-Šikonja, and Nada Lavracˇ 672 Boosting via Approaching Optimal Margin Distribution . . . . . . . . Chuan Liu and Shizhong Liao 684 o-HETM: An Online Hierarchical Entity Topic Model for News Streams . . Linmei Hu, Juanzi Li, Jing Zhang, and Chao Shao 696 Modeling User Interest and Community Interest in Microbloggings: An Integrated Approach. . . . . . . . . . . . . . . . . . . Tuan-Anh Hoang 708 Minimal Jumping Emerging Patterns: Computation and Practical Assessment .

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


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