Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning

2019 ◽  
pp. 38-55 ◽  
Author(s):  
Philip C. Roth
Keyword(s):  
Search Space ◽  
Communication Pattern ◽  
Communication Graphs ◽  
Search Space Pruning ◽  
Improved Accuracy

Effective Search-Space Pruning for Solvers of String Equations, Regular Expressions and Length Constraints

2015 ◽  
pp. 235-254 ◽  
Author(s):  
Yunhui Zheng ◽  
Vijay Ganesh ◽  
Sanu Subramanian ◽  
Omer Tripp ◽  
Julian Dolby ◽  
...  
Keyword(s):  
Search Space ◽  
Regular Expressions ◽  
Search Space Pruning

A method of search space pruning based on search state dominance

1994 ◽  
Vol 25 (4) ◽  
pp. 1-12
Author(s):  
Takayuki Fujino ◽  
Hideo Fujiwara
Keyword(s):  
Search Space ◽  
Search Space Pruning

Efficient Search-Space Pruning for Integrated Fusion and Tiling Transformations

2006 ◽  
pp. 215-229 ◽  
Author(s):  
Xiaoyang Gao ◽  
Sriram Krishnamoorthy ◽  
Swarup Kumar Sahoo ◽  
Chi-Chung Lam ◽  
Gerald Baumgartner ◽  
...  
Keyword(s):  
Search Space ◽  
Efficient Search ◽  
Search Space Pruning

Mining Top-K Click Stream Sequences Patterns

2016 ◽  
Vol 4 (3) ◽  
pp. 655
Author(s):  
MEHDI Haj Ali ◽  
Qun-Xiong Zhu ◽  
Yan-Lin He
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Pattern Mining ◽  
Search Space ◽  
Extensive Study ◽  
Sequential Patterns ◽  
Support Threshold ◽  
Search Space Pruning ◽  
High Support ◽  
Pruning Methods

<p><em>Sequential pattern mining, it  is not just important in data mining field , but  it is the basis of many applications .However, running applications cost time and memory, especially when dealing with dense of the dataset. Setting the proper minimum support threshold is one of the factors that consume more memory and time. However ,  it is difficult for users to get the appropriate patterns, it may present too many sequential patterns  and makes it difficult for users to comprehend the results. The problem becomes worse and worse when dealing with long click stream sequences or huge dataset. As a solution, we developed an efficient algorithm, called TopK (Top-K click stream sequence pattern mining), which employs the output as top-k patterns , K is the most important and relevant frequencies (with a high support) . However ,our algorithm based on pseudo-projection to avoid consuming more time and memory, and uses several efficient search space pruning methods together with BI-Directional Extension. Our extensive study and experiments on real click stream datasets show TopK significantly outperforms the previous algorithms.</em></p>

Sign in / Sign up

Export Citation Format

Share Document