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 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 well 2015.
The lawsuits comprise 117 paper rigorously reviewed and chosen from 405 submissions. they've been geared up in topical sections named: social networks and social media; class; computing device studying; functions; novel tools and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and vague facts; mining temporal and spatial information; function extraction and choice; mining heterogeneous, high-dimensional and sequential facts; entity answer and topic-modeling; itemset and high-performance information mining; and recommendations.
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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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
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. e. after separate feature extraction. We extract feature vector xIe from Instagram posts and extract feature vector xTe from Twitter posts respectively, and then concatenate them to form the ﬁnal feature vector xe . Note that by this method, the size of feature vector xe is nearly doubled compared to the ﬁrst 22 C. Xia et al. method. The beneﬁt of this method is that, we can extract diﬀerent features from Twitter and Instagram, and further incorporate other inhomogeneous data sources.
We put two data collectors in Instagram and Twitter monitoring and collecting useful information from the live post streams from these two social media platforms. Our previous work  uses Instagram posts to detect events with high accuracy. Unlike them, in this paper we use the posts from both of the two popular OSNs together to detect events. After detecting events, retrieving relevant content to represent existing events is a challenging problem. Focusing on Twitter content,  extracts tweets and topics for known events.
We introduce the detailed methodology and our system framework in Section 4. We analyze and discuss our experiment results in Section 5, and conclude our work in Section 6. 2 Related Work There have been plenty of research regarding detecting events or news. They can be categorized according to several aspects, including types of events, data sources and methods . Prior to detecting events from social media streams,  detect events from traditional media data. As a seminar work to this problem,  18 C.
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