Scalable Hierarchical Aggregation Protocol (SHArP): A Hardware Architecture for Efficient Data Reduction

2016 ◽  
Author(s):  
Richard L. Graham ◽  
Vladimir Koushnir ◽  
Lion Levi ◽  
Alex Margolin ◽  
Tamir Ronen ◽  
...  
Keyword(s):  
Data Reduction ◽  
Hardware Architecture ◽  
Hierarchical Aggregation ◽  
Efficient Data

scholarly journals An efficient data reduction method and its application to cluster analysis

2017 ◽  
Vol 238 ◽  
pp. 234-244 ◽  
Author(s):  
Jianpei Wang ◽  
Shihong Yue ◽  
Xiao Yu ◽  
Yaru Wang
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Efficient Data ◽  
Data Reduction Method

scholarly journals Efficient data reduction for large-scale genetic mapping

2015 ◽  
Author(s):  
Veronika Strnadová-Neeley ◽  
Aydın Buluç ◽  
Jarrod Chapman ◽  
John R. Gilbert ◽  
Joseph Gonzalez ◽  
...  
Keyword(s):  
Genetic Mapping ◽  
Data Reduction ◽  
Large Scale ◽  
Efficient Data

Efficient data reduction in multimedia data

2006 ◽  
Vol 25 (3) ◽  
pp. 359-374 ◽  
Author(s):  
Surong Wang ◽  
Manoranjan Dash ◽  
Liang-Tien Chia ◽  
Min Xu
Keyword(s):  
Data Reduction ◽  
Multimedia Data ◽  
Efficient Data

scholarly journals Efficient data abstraction using weighted IB2 prototypes

2014 ◽  
Vol 11 (2) ◽  
pp. 665-678 ◽  
Author(s):  
Stefanos Ougiaroglou ◽  
Georgios Evangelidis
Keyword(s):  
Data Reduction ◽  
Training Data ◽  
Training Dataset ◽  
Data Abstraction ◽  
Prototype Selection ◽  
Initial Training ◽  
Reduction Techniques ◽  
Efficient Data ◽  
Data Reduction Technique ◽  
Better Than

Data reduction techniques improve the efficiency of k-Nearest Neighbour classification on large datasets since they accelerate the classification process and reduce storage requirements for the training data. IB2 is an effective prototype selection data reduction technique. It selects some items from the initial training dataset and uses them as representatives (prototypes). Contrary to many other techniques, IB2 is a very fast, one-pass method that builds its reduced (condensing) set in an incremental manner. New training data can update the condensing set without the need of the ?old? removed items. This paper proposes a variation of IB2, that generates new prototypes instead of selecting them. The variation is called AIB2 and attempts to improve the efficiency of IB2 by positioning the prototypes in the center of the data areas they represent. The empirical experimental study conducted in the present work as well as the Wilcoxon signed ranks test show that AIB2 performs better than IB2.

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