A pruning algorithm for managing complexity in the solution of a class of linear non-quadratic regulator problems

2013 ◽  
Author(s):  
Huan Zhang ◽  
Peter M. Dower ◽  
William M. McEneaney
Keyword(s):  
Pruning Algorithm ◽  
Managing Complexity

Millennials, learning, and development: Managing complexity avoidance and narcissism.

2017 ◽  
Vol 20 (3) ◽  
pp. 176-193 ◽  
Author(s):  
Robert C. Giambatista ◽  
J. Duane Hoover ◽  
Lori Tribble
Keyword(s):  
Learning And Development ◽  
Managing Complexity

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

A SIFT Pruning Algorithm for Efficient Near-Duplicate Image Matching

2010 ◽  
Vol 22 (6) ◽  
pp. 1042-1049 ◽  
Author(s):  
Jinde Wang ◽  
Xiaoyan Li ◽  
Lidan Shou ◽  
Gang Chen
Keyword(s):  
Image Matching ◽  
Pruning Algorithm

scholarly journals Managing complexity and uncertainty in agricultural innovation through adaptive project design and implementation

2020 ◽  
pp. 1-16
Author(s):  
Jeffery W. Bentley ◽  
Diego Naziri ◽  
Gordon Prain ◽  
Enoch Kikulwe ◽  
Sarah Mayanja ◽  
...  
Keyword(s):  
Project Design ◽  
Agricultural Innovation ◽  
Design And Implementation ◽  
Managing Complexity

Frequent Closed Partial Orders Mining in Sequences

2013 ◽  
Vol 846-847 ◽  
pp. 1304-1307
Author(s):  
Ye Wang ◽  
Yan Jia ◽  
Lu Min Zhang
Keyword(s):  
Sequence Data ◽  
Real Data ◽  
Partial Orders ◽  
Hard Problem ◽  
Important Data ◽  
Data Set ◽  
Pruning Algorithm ◽  
Equal Chance ◽  
Np Hard Problem ◽  
General Sequences

Mining partial orders from sequence data is an important data mining task with broad applications. As partial orders mining is a NP-hard problem, many efficient pruning algorithm have been proposed. In this paper, we improve a classical algorithm of discovering frequent closed partial orders from string. For general sequences, we consider items appearing together having equal chance to calculate the detecting matrix used for pruning. Experimental evaluations from a real data set show that our algorithm can effectively mine FCPO from sequences.

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