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 information Mining, PAKDD 2015, held in Ho Chi Minh urban, Vietnam, in might 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; category; desktop 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 facts; characteristic extraction and choice; mining heterogeneous, high-dimensional and sequential info; entity answer and topic-modeling; itemset and high-performance information mining; and recommendations.
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
2 Problem Formulation Based on the description of heterogeneous social graph described earlier, here we formulate the Hop-bounded Maximum Group Friending (HMGF) tackled in this paper. Given two individuals u and v, let dE G (u, v) be the shortest path between u and v via friend edges in G. Moreover, given H ⊆ G, let w(H) denote the total weight of potential edges in H and let average weight, σ(H) = w(H) |H| denote the average weight of potential edges connected to each individual in H 4 . HMGF is formulated as follows.
Next, MaxGF starts to ﬁnd the solution in Hv with the maximized average weight, which includes |Hv | steps. Let Si+1 denote the subgraph after removing a vertex vˆi from Si in step i. That is, we set S1 = Hv initially, and at each step i vi }. , vˆi = arg minu∈Si τSi (u). This is based on the intuition that excluding vertices with low incident weights is more inclined to increase the average weight of the the remaining subgraph. Then, vˆi and its incident potential edges are removed from Si and the remaining graph is Si+1 .
Vˆl (t). If there is a large deviation between these two numbers, this signal is marked as a potential event signal. Following bursty detection, we are only interested in when the predicted number of posts is larger than the actual number of σ (t). σ ˆ (t) is the posts. Typically, we deﬁne an abnormality score as [ˆ vl (t) − vl (t)]/ˆ What Is New in Our City? A Framework for Event Extraction 21 predictive standard deviation given by GPR. It indicates the conﬁdence of the ˆ indicates stronger conﬁdence.