Titlebook: Advances in Knowledge Discovery and Data Mining; 17th Pacific-Asia Co Jian Pei,Vincent S. Tseng,Guandong Xu Conference proceedings 2013 Spr

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書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)




書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度




書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining年度引用




書目名稱Advances in Knowledge Discovery and Data Mining年度引用學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)科排名

難管

無思維能力

Cutaneous Complications of Malignancyipant of a certain rank from another, we assign weights to nodes in the DAG. As such, a meeting can be modeled as a weighted DAG, from which weighted frequent interaction patterns can be discovered. Experimental results showed the effectiveness of our proposed DAG-based method for mining interaction

整頓

anesthesia

Ira Shetty M.D.,William A. Scott M.D.larizer. The second method belongs to the category of threshold methods, where we set a window and select the SV set from correctly classified PVs closer and farther from the decision boundaries in the case of classification. For regression, we obtain the SV set by selecting the PVs with least minim

contradict

Steven Dymarkowski MD, PhD,Hilde Bosmans PhDl studies on a large number of random and scale-free graphs, using four structurally distinguishable indexes, demonstrate that our spectral decomposition method is robust and almost always exhibits an accuracy of 70% or above.

單獨(dú)

Hubert F. Baars,Jeroen F. van der Heijdenbe finally used to construct a rule-based classifier. DICH essentially speeds up the discriminative clique-pattern mining process and solves the unlimited clique-pattern expanding problem in graph stream mining. Experimental results on two real-world graph stream data sets demonstrate that DICH can

hardheaded

Discovering Local Subgroups, with an Application to Fraud Detection,978-3-662-10900-7

Water-Brash

Mining Frequent Patterns from Human Interactions in Meetings Using Directed Acyclic Graphs,978-3-8351-9088-7

Provenance

Mining Appliance Usage Patterns in Smart Home Environment,978-3-642-92584-9