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: 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 knowledge Mining, PAKDD 2015, held in Ho Chi Minh urban, Vietnam, in could 2015.

The court cases comprise 117 paper conscientiously reviewed and chosen from 405 submissions. they've been geared up in topical sections named: social networks and social media; class; computer studying; purposes; novel equipment and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and obscure info; 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 facts 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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Extra resources 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

Sample text

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).

It does not use the rich image content but heavily rely on user generated tags that What Is New in Our City? A Framework for Event Extraction 19 are not always reliable [27]. Although the work in [27] combines photo content and tags to detect events, it needs to discover landmarks first and then detect events around the landmarks. To be different, our work does not rely on landmark discovery, thus we can detect more general events. As to retrieving images, most existing methods rank images based on certain similarity measurements to a specific query, a keyword or image.

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. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making.

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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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