Genetic Network Programming based data mining method for extracting fuzzy association rules

2008 ◽  
Author(s):  
Karla Taboada ◽  
Eloy Gonzales ◽  
Kaoru Shimada ◽  
Shingo Mabu ◽  
Kotaro Hirasawa
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Genetic Network ◽  
Mining Method ◽  
Network Programming ◽  
Fuzzy Association Rules ◽  
Data Mining Method ◽  
Genetic Network Programming

Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems

2008 ◽  
Vol 12 (4) ◽  
pp. 393-403 ◽  
Author(s):  
Wei Wei ◽  
◽  
Huiyu Zhou ◽  
Kaoru Shimada ◽  
Shingo Mabu ◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Genetic Network ◽  
Network Programming ◽  
Association Rules Mining ◽  
Traffic System ◽  
Accumulation Mechanism ◽  
Traffic Systems ◽  
Comparative Relations ◽  
Genetic Network Programming

Among several methods of extracting association rules that have been reported, a new evolutionary method named Genetic Network Programming (GNP) has also shown its effectiveness for dense databases. However, the conventional GNP data mining method can not find comparative relations and hidden patterns among a large amount of data. In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover comparative association rules between different datasets. GNP is an evolutionary approach recently developed, which can evolve itself and find the optimal solutions. The objective of the comparative association rules mining is to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute values of the confidence differences of the rules obtained from different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyze the explicit and implicit patterns among a large amount of data. On the other hand, the calculation is very time-consuming, when the conventional GNP based data mining is used for the large attributes case. So, we have proposed an attributes accumulation mechanism to improve the performances. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analyzing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.

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.

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.

A Genetic Network Programming Based Method to Mine Generalized Association Rules with Ontology

2008 ◽  
Vol 12 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Guangfei Yang ◽  
◽  
Kaoru Shimada ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Association Rules ◽  
Genetic Network ◽  
Dynamic Threshold ◽  
Computation Method ◽  
Network Programming ◽  
Rule Space ◽  
Generalized Association Rules ◽  
Speed Up ◽  
Genetic Network Programming ◽  
Threshold Approach

In this paper, we propose a Genetic Network Programming based method to mine equalized association rules in multi concept layers of ontology. We first introduce ontology to facilitate building the multi concept layers and propose Dynamic Threshold Approach (DTA) to equalize the different layers. We make use of an evolutionary computation method called Genetic Network Programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space.

Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families

2006 ◽  
Vol 10 (6) ◽  
pp. 954-963 ◽  
Author(s):  
Kaoru Shimada ◽  
◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Association Rules ◽  
Genetic Network ◽  
Rule Extraction ◽  
Optimization Techniques ◽  
Network Programming ◽  
Genetic Operators ◽  
Chi Squared ◽  
Evolutionary Optimization Techniques ◽  
Rules Acquisition ◽  
Genetic Network Programming

A method of association rule mining with chi-squared test using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. The method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.

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