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

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.

Souvenir Sales Analysis using Apriori Algorithm (Case Study: Ubud-Market Transaction in March 2020)

2021 ◽  
Vol 10 (1) ◽  
pp. 73
Author(s):  
Muhammad Firyanul Rizky ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Minimum Support ◽  
Track Record ◽  
Market Transaction ◽  
Transaction Database

Ubud market is one of the largest art markets in Bali, there are many local Balinese souvenir traders and craftspeople, most of them are livelihoods depend on buying and selling local souvenirs, Since the Covid-19 pandemic entered in April 2020, Ubud market traders have started to close their business and hoping economic recoveryin future. The author tries to do a track record of souvenir sales transactions in Ubud market to find the last sales pattern before the traders closes their business to give a solution for marketing strategies in future. The sales transaction data will just become meaningless trash if it’s useless.. To get use information about the products that are most sold out at Ubud Market from the transaction database, the author uses the Apriori algorithm. This study was determined final rules on 2 itemset combination, If buying Manik-Manik Craft, Also buy Barong Shirt with the highest confidence 70% and Minimum Support 28%, and for 3 itemset a combination, If buying Celuk Silver, and Barong Shirt, Also buy Manik-Manik Craft with the highest confidence 37.5% and Minimum Support 12%, based on that there are 3 best-selling souvenir products, namely Barong Shirt, Manik-Manik Craft and Silver-Celuk in March 2020. Keywords: Apriori Algorithm, Data Mining, Sales Analysis, Association Rule Mining, Ubud Market.

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.

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