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 information Mining, PAKDD 2015, held in Ho Chi Minh urban, Vietnam, in might 2015.

The court cases comprise 117 paper rigorously reviewed and chosen from 405 submissions. they've been prepared in topical sections named: social networks and social media; category; computer studying; functions; novel equipment and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and vague information; mining temporal and spatial info; characteristic extraction and choice; mining heterogeneous, high-dimensional and sequential information; entity answer 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 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

1. Illustrative Example indicates that individuals u and v are likely to become friends (the edge weight w[u , v ] quantifies the likelihood). The potential edges and the corresponding edge weights can be obtained by employing a link prediction algorithm in friend recommendation. , too small a group may not work well for socialization activities. Figure 1 illustrates the social graph and the interplay of the above factors. , (c, d), is a friend edge. Figure 1(b) shows a group H1 :{a, e, f, g} which has many potential edges and thus a high total weight.

Geo-Social Group Query [6,7] extracts socially tight groups while considering certain spatial properties. The willingness optimization for social group problem in [8] selects a set of attendees for an activity while maximizing their willingness to participate. Finally, [9] finds a set of compatible members with tight social relationships in the collaboration network. Although these works find suitable attendees for activities based on existing friendship among the attendees, they ignore the likelihood of making new friends among the attendees.

However, the problem of detecting and representing local events in real time from social media data streams remains challenging. First, event-related social media data are sparse, although the volume of posts from any popular social media platform is large. Second, most current research focuses on Twitter [2], while there are various types of social media platforms which can potentially contribute to the detection problem. However, the problem to choose or combine multiple data sources to detect events is challenging due to the heterogeneity of posts from different data sources.

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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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