scholarly journals Optimal Hesitation Rule Mining using Weighted Apriori with Genetic Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 3321-3328
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Statistical Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining

Weighted Apriori algorithm practices the itemsets that are frequently generated in particular databases for statistical analysis. Traditional association rule mining only deals with the items that are actually present in the transaction and disregards the items that customers hesitated to purchase such items can considered as almost sold items that contains valuable information which can be used in enhancing the decision making capabilities. This paper focuses on the weighted apriory with genetic algorithm because with the help of weighted apriory there are some hesitation patterns are define on these rules the genetic algorithm is applied which gives the optimal results(Newly generated valid rules). This exertion portrays that if the cause of yielding the things is known and settled, we can without much of a extend expel this hesitation status of a client and thinking about recently developed rules as the intriguing ones for increase offers of the entity or item.

scholarly journals Modified classic apriori algorithm for association rule mining

2018 ◽  
Vol 7 (2.21) ◽  
pp. 414
Author(s):  
G Anitha ◽  
R A. Karthika ◽  
G Bindu ◽  
G V. Sriramakrishnan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rule ◽  
Association Rule Mining ◽  
Critical Element ◽  
Apriori Algorithm ◽  
Large Collection ◽  
Rule Mining ◽  
Hidden Patterns ◽  
World Environment

In today’s real world environment, information is the most critical element in all aspects of the life. It can be used to perform analysis and it helps to make decision making. But due to large collection of information the analysis and extraction of such useful information is tedious process which will create a major problem. In data mining, Association rules states about associations among the entities of known and unknown group and extracting hidden patterns in the data. Apriori algorithm is used for association rule mining. In this paper, due to limitations in rule condition, the algorithm was extended as new modified classic apriori algorithm which fulfills user stated minimum support and confidence constraints.  

Association Rule Mining for Job Shop Scheduling Problem Based on Genetic Algorithm

2012 ◽  
Vol 629 ◽  
pp. 730-734 ◽  
Author(s):  
Cun Liang Yan ◽  
Wei Feng Shi
Keyword(s):  
Genetic Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Rule Mining ◽  
Concept Hierarchy ◽  
Shop Scheduling

Job shop scheduling problem (JSP) is the most typical scheduling problem, In the process of JSP based on genetic algorithm (GA), large amounts of data will be produced. Mining them to find the useful information is necessary. In this paper dividing, hashing and array (DHA) association rule mining algorithm is used to find the frequent itemsets which contained in the process, and extract the corresponding association rules. Concept hierarchy is used to interpret the rules, and lots of useful rules appeared. It provides a new way for JSP study.

Two-level parallel CPU/GPU-based genetic algorithm for association rule mining

2020 ◽  
Vol 22 (2/3) ◽  
pp. 335
Author(s):  
Leila Hamdad ◽  
Zakaria Ournani ◽  
Karima Benatchba ◽  
Ahcène Bendjoudi
Keyword(s):  
Genetic Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

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.

Support-Less Association Rule Mining Using Tuple Count Cube

2007 ◽  
Vol 06 (04) ◽  
pp. 271-280
Author(s):  
Qin Ding ◽  
William Perrizo
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Cube ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
High Confidence ◽  
Support Level ◽  
Vertical Data ◽  
Frequent Item Sets ◽  
High Support

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

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