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

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 could 2015.

The court cases comprise 117 paper rigorously reviewed and chosen from 405 submissions. they've been equipped in topical sections named: social networks and social media; type; computing device studying; functions; novel tools and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and obscure information; mining temporal and spatial information; 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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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

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 court cases include 117 paper conscientiously reviewed and chosen from 405 submissions.

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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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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.

3 Problem Definition Following [32][35], we define an event as a real world activity that occurs during time period T within a geographical area L. To detect such events in real time, we define a framework as follows. The framework takes the real-time streams of posts from Instagram and Twitter as the input. The system is expected to output detected events in sequence. For each detected event, we extract its related content namely the set of related images, topics (a set of keywords), the estimated occurrence location, and the estimated occurrence time.

Proof. Due to the space constraints, we prove this theorem in the full version of this paper (available online [1]). The above theorem manifests that HMGF has no approximation algorithm. Nevertheless, we show that HMGF becomes approximable if a small error h is allowed in the hop constraint. More specifically, in Section 4, we first propose an error-bounded approximation algorithm for HMGF, which returns a solution with guaranteed σ(H), while dE G (u, v) for any two vertices u and v in H may exceed h but is always bounded by 2h.

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