Research on Mining Method of Process Knowledge Based on Ontology

2013 ◽  
Vol 401-403 ◽  
pp. 1470-1473
Author(s):  
Chun Fen Guo ◽  
Li Chen Zhuang
Keyword(s):  
Association Rule ◽  
Partial Correlation ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Process Knowledge ◽  
Concept Model ◽  
Knowledge Based ◽  
Mining Algorithm ◽  
Cross Linker ◽  
Correlation Process

Making use of hierarchical structure clearly of concept model of ontology , on the basis of Apriori algorithm and introducing cross linker method, an mining algorithm of process knowledge of association rule is proposed based on ontology. To verify the feasibility of the algorithm, partial correlation process knowledge is dicovered by this algorithm.

The Research of Improved Apriori Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2179-2184 ◽  
Author(s):  
Zhen Yun Liao ◽  
Xiu Fen Fu ◽  
Ya Guang Wang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Mining Algorithm ◽  
The Times ◽  
Frequent Item Sets ◽  
Candidate Item ◽  
Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

Application of Data Mining Technology Based on Apriori Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2036-2039
Author(s):  
Jian Xing Chen
Keyword(s):  
Data Mining ◽  
Computer Simulation ◽  
Association Rule ◽  
Computer Time ◽  
Data Mining Algorithm ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Computer Data ◽  
Mining Algorithm ◽  
Step Distance

With the continuous expansion of computer simulation scale, the demand for data mining algorithm is also more and more big. The difficulties in computer data mining technology are focused on algorithm development. Apriori algorithm is a kind of computer data mining algorithm which can greatly improve the computational efficiency. The algorithm uses association rule, which can avoid repeated frequently by layer scanning, reducing the computer time. This paper uses Apriori algorithm to design the data mining parameter optimization model of computer 3D human biology simulation, and applies to improve the step three jump. Through the simulation we found step distance appropriate, it provides technical reference for the application of computer simulation technology in sports.

An Approach to the Mining of User Focused Frequent Itemsets Based on Attention

2014 ◽  
Vol 536-537 ◽  
pp. 520-523
Author(s):  
Jia Liu ◽  
Zhen Ya Zhang ◽  
Hong Mei Cheng ◽  
Qian Sheng Fang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Frequent Itemsets ◽  
Selection Model ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Information Filter ◽  
Frequent Itemsets Mining ◽  
Log File

Usually, non trivial network visiting behaviors implied in network visiting log can be treated as the frequent itemsets or association rules if data in networking log file are transformed into transaction and technologies on association rule can be used to mine those frequent itemsets which are focused by user or some application. To mine non trivial behaviors of network visiting effectively, an attention based frequent itemsets mining method is proposed in this paper. In our proposed method, properties of users focusing is described as attention set and the early selection model of attention as information filter is referenced in the design of our method. Experimental results show that our proposed method is faster than apriori algorithm on the mining of frequent itemsets which is focused by our attention.

The Research of Improved Apriori Algorithm Application in Distance Education Platform

2013 ◽  
Vol 333-335 ◽  
pp. 1319-1323
Author(s):  
Xin Wang ◽  
Jian Wei Wang ◽  
Long Hei
Keyword(s):  
Distance Education ◽  
Association Rule ◽  
Association Rule Mining ◽  
Processing Time ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Path Distance ◽  
Transaction Database ◽  
Education Platform

This paper points out the bottleneck of classical Apriori algorithm, presents an improved association rule mining algorithm based on Apriori algorithm.The new algorithm is based on pruing away the itemsets whose support degree is less than minsupport to reduce the number of itemsets in the transaction database. At the same time the new algorithm change the candidate_gen function to generate a continuous access page. According to the running result of the algorithm, the processing time of mining is decreased and the efficiency of algorithm has improved.Whats more, the new algorithm can find the learners frequent traversal path to improve the intelligence of the distance education platform. Keywords: Associaion Rules;Apriori Algorithm; Frequent Traversal Path;Distance Education Platform

An Efficient Communication Network SDH Alarm Association Rule Mining Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 1870-1873
Author(s):  
Hui Sheng Gao ◽  
Ying Min Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Event Window ◽  
Mining Algorithm ◽  
Efficient Communication ◽  
Algorithm Framework

WINEPI algorithm is kind of data mining technology that is widely used in alarm association rules mining. Based on the classic WINEPI algorithm, we apply event window instead of time window to improve the exploration result, meanwhile we use FP-Growth algorithm framework instead of Apriori algorithm framework , thus improving efficiency. Based on the alarm time attribute we find interesting alarm association rules further. Experiments show that compared with the classic WINEPI algorithm our improved approach have advantages in reducing the mining error rate and gaining more interesting alarm association rules.

scholarly journals Use of Association Rule Mining within the Framework of a Customer-Oriented Approach

2016 ◽  
Vol 12 (9) ◽  
pp. 81
Author(s):  
Mehmet Ali Alan ◽  
Ali Riza Ince
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Customer Orientation ◽  
Apriori Algorithm ◽  
Alternative Technique ◽  
Mining Method ◽  
Rule Mining ◽  
Purchasing Behaviour ◽  
Existing Data ◽  
Oriented Approach

In this study, it was aimed to investigate whether an association rule exists between the products sold, using the sales data of a supermarket with the data mining method within the framework of a customer-oriented approach. For this purpose, the Association Rule Mining Method was used, and analyses were carried out on existing data with the Apriori Algorithm that is widely used in this method. Various association rules were determined between the products sold as a result of these analyses. It was assessed that Association Rule Mining is an alternative technique to proactive customer orientation by revealing the latent purchasing behaviour patterns of the customers.

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