The Application of Apriori Algorithm in Analysis on Admitted Students of Colleges and Universities

2013 ◽  
Vol 321-324 ◽  
pp. 2578-2582
Author(s):  
Qian Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Sample Data ◽  
Mining Association Rules

This paper examined the application of Apriori algorithm in extracting association rules in data mining by sample data on student enrollments. It studied the data mining techniques for extraction of association rules, analyzed the correlation between specialties and characteristics of admitted students, and evaluated the algorithm for mining association rules, in which the minimum support was 30% and the minimum confidence was 40%.

Research and Application of Apriori Algorithm for Mining Association Rules

2014 ◽  
Vol 1079-1080 ◽  
pp. 737-742
Author(s):  
Yi Yong Ye
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Web Mining ◽  
Recommendation System ◽  
Apriori Algorithm ◽  
Mining Algorithm ◽  
Common Technique ◽  
Mining Association Rules

For large amounts of data generated by the e-commerceplatform, combining with the actual needs of e-commerce recommendation system,make research on a common technique of association rules which orientede-commerce Web mining association analysis, introduces the association rules ofApriori mining algorithm, and the specific application of Apriori algorithm isanalyzed through a practical example, Finally, point out the shortcomings ofclassical Apriori algorithm, and gives directions for improvement.

Improvement of Apriori Algorithm Based on the User Interest and the Importance of Itemsets

2010 ◽  
Vol 121-122 ◽  
pp. 540-545
Author(s):  
Yue Shun He ◽  
Xiang Li
Keyword(s):  
Association Rules ◽  
Apriori Algorithm ◽  
Storage Space ◽  
User Interest ◽  
Support Functions ◽  
Classical Algorithm ◽  
Mining Association Rules ◽  
The Many ◽  
Mining Algorithms

Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms existed "item sets generation bottleneck" problem, and are very time-consuming. An enhance algorithm associating which is based on the user interest and the importance of itemsets is put forward by the paper, incorporate item that user is interested in into the itemsets as a seed item, then scan the database, incorporate all other items which are in the same transaction into itemsets, Construct user interest itemsets, reduce unnecessary itemsets; through the design of the support functions algorithm not only considered the frequency of itemsets, but also consider different importance between different itemsets. The new algorithm reduces the storage space, improves the efficiency and accuracy of the algorithm.

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