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.

Genetic Network Programming with Acquisition Mechanisms of Association Rules

2006 ◽  
Vol 10 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Kaoru Shimada ◽  
◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Operators ◽  
Rule Mining ◽  
Rule Acquisition ◽  
Chi Squared ◽  
Genetic Network Programming

A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of association rule extraction. The proposed mechanisms can calculate measurements of association rules directly using GNP, and measure the significance of the association via the chi-squared test. Users can define the conditions of importance of association rules flexibly, which include the chi-squared value and the number of attributes in a rule. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. Besides, our method can contain negation of attributes in association rules and suit association rule mining from dense databases. In this paper, we describe an extended algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results.

scholarly journals Adaptation and Self-Adaptation Mechanisms in Genetic Network Programming for Mining Association Rules

2007 ◽  
Vol 11 (3) ◽  
pp. 343-353 ◽  
Author(s):  
Karla Taboada ◽  
◽  
Eloy Gonzales ◽  
Kaoru Shimada ◽  
Shingo Mabu ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Directed Graphs ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Self Adaptation ◽  
Crossover And Mutation ◽  
Genetic Network Programming

In this paper we propose a method of association rule mining using Genetic Network Programming (GNP) with adaptive and self-adaptive mechanisms of genetic operators in order to improve the performance of association rule extraction systems. GNP is one of the evolutionary methods, whose directed graphs are evolved to find a solution as individuals. Adaptation behavior in GNP is related to adjust the setting of control parameters such as the proportion of crossover and mutation. The aim is not only to find suitable adjustments but to do it efficiently. Regarding to self-adaptation, the algorithm controls the setting of these parameters themselves – embedding them into an individual’s genome and evolving them, and it usually changes the structure of the evolution which is typically static. Specifically, self-adaptation of crossover and mutation operators in GNP aiming to change the rate of them by evolution is studied in this paper. Our method based on GNP can measure the significance of the association via the chi-squared test and obtain a sufficient number of important association rules. Extracted association rules are stored in a pool all together through generations and reflected in three genetic operators as acquired information.

scholarly journals Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems

2011 ◽  
Vol 15 (5) ◽  
pp. 515-524
Author(s):  
Yafei Xing ◽  
◽  
Singo Mabu ◽  
Lian Yuzhu ◽  
Kotaro Hirasawa
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Genetic Network ◽  
Rule Extraction ◽  
Network Programming ◽  
Evolutionary Computations ◽  
Stock Trading ◽  
Trading Rules ◽  
Rules Extraction ◽  
Genetic Network Programming

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by Genetic Network Programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that themulti-order rules extracted by GNP is more effective than the 1st order rules for stock trading.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document