CBERS-02 Remote Sensing Data Mining Using Decision Tree Algorithm

2008 ◽  
Author(s):  
Xingping Wen ◽  
Guangdao Hu ◽  
Xiaofeng Yang
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Remote Sensing Data ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Sensing Data

An Analysis of Factors Influencing Foreign Language Self-Efficacy Based on C5.0 Decision Tree Algorithm in Data Mining

2014 ◽  
Vol 538 ◽  
pp. 460-464
Author(s):  
Xue Li
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Foreign Language ◽  
Language Learning ◽  
Learning Strategies ◽  
Self Efficacy ◽  
Foreign Language Learning ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C5.0 Decision Tree

Based on inter-correlation and permeability among disciplines, the author makes an attempt to apply the information science to cognitive linguistics to provide a new perspective for the study of foreign languages. The correlation between self-efficacy and such four factors as anxiety, learning strategies, motivation and learners’ past achievement is analyzed by means of data mining and the extent to which the above factors affect self-efficacy in language learning is explored in this paper. The paper employs the decision tree algorithm in SPSS Clementine. C5.0 decision tree algorithm is adopted to analyze data in the study. The results are elicited from the researches carried out in this paper. The increased anxiety is bound to weaken learners’ motivation over time. It is obvious that learners have low self-efficacy. It is very important to employ strategies in foreign language learning. Ignorance of using learning strategies may result in unplanned learning with unsatisfactory achievements in spite of more efforts involved. Self-efficacy in foreign language learning may be weakened accordingly. Learners’ past achievement is a reference dimension in measuring self-efficacy with weaker influence.

Application of the Parallel Spatial Association Rule in Remote Sensing Data Mining

2012 ◽  
Vol 500 ◽  
pp. 598-602
Author(s):  
Jun Ma ◽  
Dong Dong Zhang
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Association Rule ◽  
Spatial Association ◽  
Remote Sensing Data ◽  
Inner Product ◽  
Data Mining Algorithm ◽  
Sensing Data ◽  
The Common ◽  
Frequent Item Sets

Since the remote sensing data are multi-resources and massive, the common data mining algorithm cannot effectively discover the knowledge what people want to know. However, spatial association rule can solve the problem of inefficiency in remote sensing data mining. This paper gives an algorithm to compute the frequent item sets though a method like calculating vectors inner-product. And the algorithm will introduce pruning in the whole running. It reduces the time and resources consumption effectively

Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective

2011 ◽  
Vol 262 (8) ◽  
pp. 1597-1607 ◽  
Author(s):  
Carmen Quintano ◽  
Alfonso Fernández-Manso ◽  
Alfred Stein ◽  
Wietske Bijker
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Forest Fires ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Area Burned ◽  
Mediterranean Countries

scholarly journals CUDT: A CUDA Based Decision Tree Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Win-Tsung Lo ◽  
Yue-Shan Chang ◽  
Ruey-Kai Sheu ◽  
Chun-Chieh Chiu ◽  
Shyan-Ming Yuan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
New Technology ◽  
Large Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Tree Algorithm ◽  
Ubiquitous Sensing ◽  
Device Architecture ◽  
Huge Data

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

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