scholarly journals Federated Mining of Interesting Association Rules Over EHRs

2021 ◽  
Author(s):  
Carlos Molina ◽  
Belen Prados-Suarez ◽  
Beatriz Martinez-Sanchez
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Transfer ◽  
Mining Method ◽  
Data Mining Method ◽  
Fair Principles ◽  
Different Sources

Federated learning has a great potential to create solutions working over different sources without data transfer. However current federated methods are not explainable nor auditable. In this paper we propose a Federated data mining method to discover association rules. More accurately, we define what we consider as interesting itemsets and propose an algorithm to obtain them. This approach facilitates the interoperability and reusability, and it is based on the accessibility to data. These properties are quite aligned with the FAIR principles.

Research and Simulation of Data Mining Method Based on the Association Rules Algorithm

2014 ◽  
Vol 651-653 ◽  
pp. 2185-2188
Author(s):  
Jin Ping Zou ◽  
Xiao Dong Xie
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rules ◽  
Experimental Results ◽  
Mining Method ◽  
Accurate Data ◽  
Minimum Support ◽  
Data Mining Method

the accurate data mining problem is studied in this paper. With the increasing of data attributes, degree of complexity of the data storage is also increased, resulting in that in data mining process, the complexity of computation is too high, reducing the convergence of the data mining method, thereby reducing the efficiency of data mining. To this end, this paper presents a data mining method based on association rules algorithm. The data is made simplified processing, to obtain the association rules between data which provides the basis for data mining. According to the association rules between the data, the data in line with the minimum support degree is calculated, to achieve accurate data mining. Experimental results show that the proposed algorithm for data mining, can improve mining efficiency, and achieve the desired results.

An Data Ming Method Based on AHP and Apriori

2012 ◽  
Vol 198-199 ◽  
pp. 431-434
Author(s):  
Hua Lin Ma
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet ◽  
Mining Method ◽  
Data Mining Method ◽  
Data Ming

As the current personalized recommendation methods of Internet bookstore are limited too much in function, this paper proposes a kind of Internet bookstore data mining method based on “Strategic”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. The experimental results indicate that the Internet bookstore recommendation method is feasible.

Mining Mobile Telecommunication Services Using Apriori Algorithm

2011 ◽  
Vol 291-294 ◽  
pp. 2369-2373
Author(s):  
Ming Hua Jiang
Keyword(s):  
Data Mining ◽  
Mobile Phone ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Mobile Telecommunication ◽  
Telecommunication Services ◽  
Data Mining Method ◽  
Maximum Profit

This paper introduces the mobile phone services and association rules, and a data mining method Apriori Algorithm is introduced and uses it to mine the frequency of mobile phone services,and referrals to salespersons to develop the mobile phone services which woud lead to maximum profit,and help mobile telecommunicaiton company to make decision on seveices planning.

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