Mining Customers' Spatio-Temporal Behavior Data Using Topographic Unsupervised Learning

Author(s):  
Guénaël Cabanes ◽  
Younès Bennani ◽  
Frédéric Dufau-Joël
Author(s):  
Ahmed Abusnaina ◽  
Mohammed Abuhamad ◽  
DaeHun Nyang ◽  
Songqing Chen ◽  
An Wang ◽  
...  

2020 ◽  
Vol 25 (9) ◽  
pp. 931-947
Author(s):  
Ding Xu ◽  
Li Cong ◽  
Geoffrey Wall

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e19397 ◽  
Author(s):  
Denis B. Rosemberg ◽  
Eduardo P. Rico ◽  
Ben Hur M. Mussulini ◽  
Ângelo L. Piato ◽  
Maria E. Calcagnotto ◽  
...  

1989 ◽  
Vol 44 (11) ◽  
pp. 1046-1050 ◽  
Author(s):  
J. Parisi ◽  
J. Peinke ◽  
R. P. Huebener

We study the cooperative spatio-temporal behavior of semiconductor breakdown via both probabilistic and dynamical characterization methods (fractal dimensions, entropies, Lyapunov exponents, and the corresponding scaling functions). Agreement between the results obtained from the different numerical concepts (e.g., verification of the Kaplan-Yorke conjecture and the Newhouse- Ruelle-Takens theorem) gives a self-consistent picture of the physical situation investigated. As a consequence, the affirmed chaotic hierarchy of generalized horseshoe-type strange attractors may be ascribed to weak nonlinear coupling between competing localized oscillation centers intrinsic to the present semiconductor system


2017 ◽  
Vol 6 (5) ◽  
pp. 151 ◽  
Author(s):  
Luliang Tang ◽  
Qianqian Zou ◽  
Xia Zhang ◽  
Chang Ren ◽  
Qingquan Li

2002 ◽  
Vol 49 (1-4) ◽  
pp. 147-163 ◽  
Author(s):  
Mengzhi Wang ◽  
Anastassia Ailamaki ◽  
Christos Faloutsos

2016 ◽  
Vol 23 (11) ◽  
pp. 112304 ◽  
Author(s):  
R. Lugones ◽  
P. Dmitruk ◽  
P. D. Mininni ◽  
M. Wan ◽  
W. H. Matthaeus

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