In this paper, we propose a rule discovery method that can reveal a combination of attributes that provide characteristic distribution of two consecutive variables of interest directly at high speed in a database having many attributes. In numerical association rule mining (NARM), when using association rules that handle consecutive numerical data values, it is difficult to heuristically extract rules that focus on statistical distributions of numerical data. The proposed method enables quick discovery of the number of rules necessary for prediction purposes using evolutionary calculations characterized by a network structure and a strategy to pool solutions throughout generations. This effectively finds attribute combinations in which the values taken by two consecutive variables of interest are both narrow ranges and can address instance-based two-dimensional regression problems in a short time. As an evaluation experiment, a prediction task using musical data linked with map data was carried out, and the discovery condition of the flexible rule was set. This resulted in realizing a high coverage rate in the instance-based regression problem, and the proposed method was effective in rule discovery based on the statistical distribution in NARM.

An extended method to discover exceptional association rule sets from incomplete databases is proposed. In an exceptional rule set, each itemset X, Y has a weak or no statistical relation to class C; however, the join of X and Y has a strong relation to C. An exceptional rule set can be used to infer long rules for the join of X and Y and to discover rare rules. The proposed method calculates the rule evaluation odds ratio directly. In this study, the method is applied to rule discovery using a database of community-dwelling elderly. Experimental results demonstrate that the proposed method can help discover interesting rare rules and exceptional association rule sets. The results show the effectiveness of the proposed method in the fields of medicine and health care.

In this paper, a dynamic traffic management model has been proposed to alleviate the traffic congestion and improve the efficiency of the traffic systems in global perspective. The proposed traffic management model is applied to the large scale microscopic simulator SOUND/4U based on the real world road network of Kurosaki, Kitakyushu in Japan. All the vehicles in the simulator follow the direction from the route guidance of the dynamic traffic management model, in which the extended Q value-based Dynamic Programming with Boltzmann Distribution and the time-varying traffic information are used to generate the routes from the origins to destinations. The simulation results show that the proposed Q value-based Dynamic Programming with Boltzmann Distribution could reduce the traffic congestion and improve the efficiency of the whole traffic system effectively compared with the greedy method in the real world road network.

This paper describes a methodology and results of traffic prediction by extracting important time related association rules using an evolutionary algorithm named Genetic Network Programming(GNP). The extracted rules provides an useful mean to investigate the future traffic density of traffic networks and hence to develop traffic navigation systems. The proposed methodology is implemented and experimentally evaluated using a large scale real-time traffic simulator SOUND/4U. The routing algorithm combined with the traffic prediction results is studied using the environment of SOUND/4U.

As the Internet services spread all over the world, many kinds and a large number of security threats are increasing. Therefore, intrusion detection systems, which can effectively detect intrusion accesses, have attracted attention. This paper describes a novel fuzzy class-association-rule mining method based on genetic network programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization technique, which uses directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database that contains both discrete and continuous attributes and also extract many important class-association rules that contribute to enhancing detection ability. Therefore, the proposed method can be flexibly applied to both misuse and anomaly detection in network-intrusion-detection problems. Experimental results with KDD99Cup and DARPA98 databases from MIT Lincoln Laboratory show that the proposed method provides competitively high detection rates compared with other machine-learning techniques and GNP with crisp data mining.

As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), a new approach named Genetic Network Programming (GNP) has been proposed in the evolutionary computation field. GNP uses multiple reusable nodes to construct directed-graph structures to represent its solutions. Recently, many research has clarified that GNP can work well in data mining area. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to solve traffic prediction problems using class association rule mining. In GNP-EDAs, a probabilistic model is constructed by estimating the probability distribution from the selected elite individuals of the previous generation to replace the conventional genetic operators, such as crossover and mutation. The probabilistic model is capable of enhancing the evolution to achieve the ultimate objective. In this paper, two methods are proposed based on extracting the probabilistic information on the node connections and node transitions of GNP-EDAs to construct the probabilistic model. A comparative study of the proposed paradigm and the conventional GNP is made to solve the traffic prediction problems using class association rule mining. The simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the number of the candidate class association rules increases. And the classification accuracy of the proposed method shows good results in traffic prediction systems.

Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a method of Generalized Association Rule Mining using Genetic Network Programming (GNP) with MBFP(Multi-Branch and Full-Pathes) processing mechanism has been introduced in order to find time related sequential rules more efficiently. GNP represents solutions as directed graph structures, thus has compact structure and partially observable Markov decision process. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction and its usage. The generalized algorithm which can find the important time related association rules has been proposed and experimental results are presented considering how to use the rules to predict the future traffic volume and also how to use the traffic prediction in the optimal search problem.

Most of the classification methods proposed produces too many rules for humans to read over, that is, the number of generated rules is thousands or millions which means complex and hardly understandable for the users.

A method of association rule mining from incomplete databases is proposed using Genetic Network Programming (GNP). GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. An incomplete database includes missing data in some tuples. Previous rule mining approaches cannot handle incomplete data directly. The proposed method can extract rules directly from incomplete data without generating frequent itemsets used in conventional approaches. In this paper, the proposed method is combined with difference rule mining using GNP for flexible association analysis. We have evaluated the performances of the rule extraction from incomplete medical datasets generated by random missing values. In addition, artificial missing values for privacy hiding are considered using the proposed method.

In order to alleviate the congestion in modern metropolises with over crowded traffics and improve the efficiency of Intelligent Transportation Systems, three temperature parameter control methods of Q value-based Dynamic Programming with Boltzmann Distribution have been proposed in this paper. The simulation result shows that each method has its own areas of expertise depending on its features and all of the methods could improve the efficiency of the traffic system comparing with the conventional Greedy Method.

Genetic Network Programming (GNP) based time related association rules mining method provides an useful mean to investigate future traffic volumes of road networks and hence helps to develop traffic navigation systems. Further improvements have been proposed in this paper about the time related association rule mining using generalized GNP with Accuracy Validation. For better adapting to the real-time traffic situations of the large scale simulator, the mechanism of Accuracy Validation is studied. The aim of this algorithm is to better handle association rule extraction using prediction accuracy as criteria and guide the whole evolution process. The generalized algorithm which can find the important time related association rules is described and experimental results are presented considering a traffic prediction problem using the database provided by a large scale simulator SOUND/4U.

This paper describes the use of a new evolutionary method named Genetic Relation Algorithm (GRA) for reducing the number of class association rules extracted by other methods such as Apriori, Genetic Network Programming(GNP), etc. The purpose is to generate a small number of class association rules in order to delete irrelevant and redundant rules. A reduced rule set has advantages as it provides only useful rules and makes its analysis more efficient. Our approach is based on evaluating the distances between rules for evolving GRA and also evaluating the distances between the data in the test set and the rules for classification. Two matching criteria are presented: complete match and partial match. The classification accuracy obtained by our method is better compared to other reported results in multi-class datasets showing an impressive reduction rate.

This paper presents a novel classification approach that integrates fuzzy classification rules and Genetic Network Programming (GNP). A fuzzy discretization technique is applied to transform the dataset, particularly for dealing with quantitative attributes. GNP is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees of Genetic Algorithms (GA) and Genetic Programming (GP) respectively. This feature contributes to creating quite compact programs and implicitly memorizing past action sequences. Therefore, in the proposed method, taking the GNP's structure into account 1) extraction of fuzzy classification rules is done without identifying frequent itemsets used in most Apriori-based data mining algorithms, 2) calculation of the support, confidence and x2value is made in order to quantify the significance of the rules to be integrated into the classifier, 3) fuzzy membership values are used for fuzzy classification rules extraction, 4) fuzzy rules are mined through generations and stored in a general pool. On the other hand, parameters of the membership functions are evolved by non-uniform mutation in order to perform a more global search in the space of candidate membership functions. The performance of our algorithm has been compared with other relevant algorithms and the experimental results have shown the advantages and effectiveness of the proposed model.

Time related association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, we introduce a method of generalized association rule mining using genetic network programming (GNP) with time series processing mechanism in order to find time related sequential rules efficiently. GNP represents solutions as directed graph structures, thus has compact structure and implicit memory function. The inherent features of GNP make it possible for GNP to work well especially in dynamic environments. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time related association rules is described and experimental results are presented considering a traffic prediction problem.

In this paper, we propose a Genetic Network Programming (GNP) based ranking method to improve the accuracy of Classification Based on Association Rule(CBA). We start from an empirical phenomenon, that is, the accuracy could be improved by changing the ranking of rules in CBA. Then, we apply GNP to build a model, namely RuleRank, to find good ranking equations to rank association rules in CBA. The simulation results show that RuleRank could improve the accuracy of CBA effectively.

In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.

Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques, which uses directed graph structures as genes instead of strings (Genetic Algorithm) or trees (Genetic Programming), leading to creating compact programs and implicitly memorizing past action sequences. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database which contains both discrete and continuous attributes. And it can be flexibly applied to both misuse and anomaly detection in Network Intrusion Detection Problem. Experimental results with KDD99Cup and DAPRA98 databases from MIT Lincoln Laboratory show that the proposed method provides a competitively high detection rate compared with other machine learning techniques.

Association rule-based classification is one of the most important data mining techniques applied to many scientific problems. In the last few years, extensive research has been carried out to develop enhanced methods and obtained higher classification accuracies than traditional classifiers. However, the current studies show that the association rule-based classifiers may also suffer some problems inherited from association rule mining such as handling of (1) continuous data and (2) the support/confidence framework. In this paper, a novel fuzzy classification model based on genetic network programming (GNP) that can deal with the above problems has been proposed. GNP is one of the evolutionary optimization algorithms that uses directed graph structures as solutions instead of strings (genetic algorithms) or trees (genetic programming). Therefore, GNP can deal with more complex problems by using the higher expression ability of graph structures. The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model.

In this paper, we propose a heuristic method trying to improve the efficiency of traffic systems in the global perspective, where the optimal traveling time for each origin-destination (OD) pair is calculated by extended Q value-based dynamic programming and the global optimum routes are produced by adjusting the temperature parameter in Boltzmann distribution. The key point is that the temperature parameter for each section is not identical, but constantly changing with the traffic of the section, which enables the diversified routing strategy depending on the latest traffics. In addition, the simulation results show that comparing with the greedy strategy and constant temperature parameter strategy, the proposed method, i.e., temperature parameter control strategy of the Q value-based dynamic programming with Boltzmann distribution, could reduce the traffic congestion effectively and minimize the negative impact of the information update interval by adopting suitable temperature parameter control strategy.

In this paper, backward time related association rule mining using genetic network programming (GNP) with database rearrangement is introduced in order to find time related sequential association from time related databases effectively and efficiently. GNP is a kind of human brain like evolutionary model which represents solutions as directed graph structures. The concept of database rearrangement to better handle association rule extraction from the databases in the traffic volume prediction problems is proposed. The proposed algorithm and experimental results are also included.

The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied genetic network programming (GNP) to creat a stock trading model. In this paper, we present a new method called real time updating genetic network programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to creat the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time uptating. We also compare the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

The number of association rules generated during the data mining process is generally very large, that is, an association rule mining algorithm could generate thousands or millions of rules. However, only a small number of rules are likely to be of any interest to the domain expert analyzing the data, i.e., many of the rules are either irrelevant or obvious. Therefore, techniques for evaluating the relevance and usefulness of discovered patterns are required. The aim of this paper is to propose a new method for evaluating the relevance and usefulness of discovered association rules by reducing the number of rules extracted using an evolutionary method named Genetic Relation Programming (GRP). The algorithm evaluates the relationships between the rules at each generation using a specific measure of distance and gives the best set of rules at the final generation. The efficiency of the proposed method is compared with other conventional methods and it is clarified that the proposed method shows comparable accuracy with others.

In this paper, a new data mining algorithm is proposed to enhance the capability of exploring interesting knowledge from databases with continuous values. The al gorithm integrates Fuzzy Set Theory and “Genetic Network Programming (GNP)” to find interesting fuzzy association rules from given transaction data. GNP is a novel evolutionary optimization technique, which uses directed graph structures as gene instead of strings (Genetic Algorithms) or trees (Genetic Programming), contributing to creating quite compact programs and implicitly memorizing past action sequences. We adopt the Fuzzy Set Theory to mine associate rules that can be expressed in linguistic terms, which are more natural and understandable for human beings. The proposed method can measure the significance of the extracted association rules using support, confidence and χ2 test, and obtains a sufficient number of important association rules in a short time. Experiments conducted on real world databases are also made to verify the performances of the proposed method.

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 association rules between different datasets. GNP is an evolutionary approach which can evolve itself and find the optimal solutions. The motivation of the comparative association rules mining method is to use the data mining approach 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 difference of confidences among 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. For the large attributes case, the calculation is very time-consuming, when the conventional GNP based data mining is used. So, we have proposed an attribute accumulation mechanism to improve the performance. 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.

Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the WWW by search engines. We build an ontology to describe the concepts and relationships in the research domain and mine association rules by Genetic Network Programming from the database where the attributes are concepts in ontology. By considering both the semantic similarity between the rules and the keywords, and the statistical information like support, confidence, chi-squared value, we could rank the rules by a new method named RuleRank, where genetic algorithm is applied to adjust the parameters and the optimal ranking model is built for the user. Experiments show that our approach is effective for the users to find what they want.

Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. As many papers have demonstrated that GNP which has a directed graph structure can deal with dynamic environments very efficiently and effectively. It can be used in many areas such as data mining, forecasting stock markets, elevator system problems, etc. In order to improve GNP’s performance further, this paper proposes a method called GNP with Rules. The aim of the proposal method is to balance exploitation and exploration, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposal method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tile-world was used as a simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.

We propose a method of association rule mining using genetic network programming (GNP) with time series processing mechanism and attribute accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. We suppose that, the database consists of a large number of attributes based on time series. In order to deal with databases which have a large number of attributes, GNP individual accumulates better attributes in it gradually round by round, and the rules of each round are stored in the Small Rule Pool using hash method, and the new rules will be finally stored in the Big Rule Pool. The aim of this paper is to better handle association rule extraction of the database in many time-related applications especially in the traffic prediction problem. In this paper, the algorithm capable of finding the important time related association rules is described and experimental results considering a traffic prediction problem are presented.

Double-deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up-peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up-peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all of the traffic flows vary continuously during a day, an optimized controller of DDES is required to adapt the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using genetic network programming (GNP) based on our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A traffic flow judgment part was introduced into the GNP framework of DDES controller, and the different parts of GNP were expected to be functionally localized by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the work time of a typical office building.

Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named "Genetic Network Programming" (GNP) has been developed and applied to the stock market recently. In this paper a portfolio optimization system based on Genetic Network Programming with control nodes is presented, which makes use of the information from Technical Indices and Candlestick Chart. The proposed optimization system, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes outperforms other traditional models and Buy&Hold method in terms of both accuracy and efficiency, and its effectiveness has been confirmed.

A new evolutionary method named "Genetic Network Programming with Control Nodes, GNPcn" has been proposed and applied to determine the timing of buying and selling stocks. GNPcn represents its solution as a directed graph structure which has some useful features inherently. For example, GNPcn has the implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so highly compact graph structures can be made. GNPcn can improve the strategy of buying and selling stocks of multi issues. Its effectiveness is confirmed by some simulations.

This paper proposes an algorithm to solve multi-objective problems by Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) which uses an external population, called pareto vector set P, in genetic operators. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isn't so efficient. To increase its efficiency, we combined RasID with genetic algorithms (GA), which are superior at finding global optima. In this paper, RasID-GA adapted to solve multi-objective optimization problems aims to find the Pareto-Optimal solutions using a non dominated sorting. The results are compared with the NSGA-II algorithm by simulating well known benchmarks.

Genetic network programming (GNP), one of the extended evolutionary algorithms was proposed, whose gene is constructed by the directed graph. GNP can perform a global searching, but it lacks of the exploitation ability. Since the behavior of GNP is characterized by the balance between exploitation and exploration in the search space, we proposed a hybrid algorithm in this paper that combines GNP with ant colony optimization (ACO). The genetic operators are operated using the pheromone information in some special generations. We applied the proposed hybrid algorithm to a complicated real world problem, that is, elevator group supervisory control system (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

This paper introduces an iterative Q value updating algorithm based on dynamic programming for searching the optimal route and its optimal traveling time for a given origin-destination (OD) pair of road networks. The proposed algorithm finds the optimal route based on the local traveling time information available at each adjacent intersection. For all the intersections of the road network, Q values are introduced for determining the optimal route. When the Q values converge, we can get the optimal route from multiple sources to single destination. If there exist multiple routes with the same traveling time, the proposed method can find all of it. When the traveling time of the road links change, an alternative optimal route is found starting with the already obtained Q values. The proposed method was applied to a grid like road network and the results show that the optimal route can be found in a small number of iterations.

Among several methods of extracting association rules that have been reported, a new evolutionary computation method named Genetic Network Programming (GNP) has also shown its effectiveness for small datasets that have a relatively small number of attributes. The aim of this paper is to propose a new method to extract association rules from large and dense datasets with a huge amount of attributes using GNP It consists of two level of processing. Server Level where conventional GNP based mining method runs in parallel and Client Level where files are considered as individuals and genetic operations are carried out over them. The algorithm starts dividing the large dataset into small datasets with appropiate size, and then each of them are dealt with GNP in parallel processing. The new association rules obtained in each generation are stored in a general global pool. We compared several genetic operators applied to the individuals in the Global Level. The proposed method showed remarkable improvements on simulations.

This paper applies a Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) to neural network training. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for optimization. Neural networks are widely used in pattern recognition, system modeling, prediction and other areas. Although most neural network training uses gradient based schemes such as wellknown back-propagation (BP), but sometimes BP is easily dropped into local minima. In this paper, we train multi-branch neural networks using RasID-GA with constraint coefficient C by which the feasible solution space is controlled. In addition, we use Mackey-Glass time prediction to test a generalization ability of the proposed method.

Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval to a discrete numerical value. However, by means of methods of discretization, it is difficult to get highest attribute interdependency and at the same time to get lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named “Genetic Network Programming (GNP)” that can deal with continues values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolve them in order to find a solution; this feature contributes to creating quite compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rule is measured by the use of the chi-squared test and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real life database suggest that the proposed method provides an effective technique for handling continuous attributes.

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. The simulation results show that our method could efficiently find some rules even in the early generations.

Several methods of extracting association rules have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has also been developed recently and its efectiveness is shown for small datasets. However, it has not been tested for large datasets, particularly in datasets with a large number of attributes. The aim of this paper is to extract association rules from large and dense datasets using GNP considering a real world database with a huge number of attributes. We propose a new method where a large database is divided into many small datasets, then each GNP deals with one dataset having attributes with appropiate size, which was selected randomly from a large dataset and generated genetically. These GNPs are processed in parallel. We then propose some new genetic operations to improve the number of rules extracted and their quality as well. The proposed method improves remarkably on simulations.

Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. However, by means of methods of discretization, it is difficult to get highest attribute interdependency and at the same time to get lowest number of intervals. We propose a method using a new graph-based evolutionary algorithm named Network Programming (GNP) that can deal with continues values directly, that is, without using any discretization method as a preprocessing step. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as solutions and is composed of three kinds of nodes: start node, judgment node and processing node. Once GNP is booted up, firstly the execution starts from the start node, secondly the next node to be executed is determined according to the judgment and connection from the current activated node. The features of GNP are described as follows. First, it is possible to reuse nodes; because of this, the structure is compact. Second, GNP can find solutions of problems without bloat, which can be sometimes found in Genetic Programming (GP), because of the fixed number of nodes in GNP. Third, nodes that are not used at the current program executions will be used for future evolution. Fourth, GNP is able to cope with partially observable Markov processes. In this paper, we propose a method that can deal with continuous attributes, where attributes in databases correspond to judgment nodes in GNP and each continuous attribute is checked whether its value is greater than a threshold value and the association rules are represented as the connections of the judgment nodes. Threshold ai is firstly determined by calculating the mean µi and standard deviation si of all attribute values of Ai. Then, initial threshold ai is selected randomly between the interval [µi - aisi, µi + aisi] where ai is a parameter to determine the range of the interval. Once the threshold ai is selected for all attributes, each value of the attribute Ai is checked if it is greater than the threshold ai in the judgment nodes of the proposed method. In addition to that, the threshold ai is also evolved by mutation between [µi - aisi, µi + aisi] in every generation in order to obtain as many association rules as possible. The features of the proposed method are as follows compared with other methods: 1) Extracts rules without identifying frequent itemsets used in Apriori-like mining methods. 2) Stores extracted important association rules in a pool all together through generations. 3) Measures the significance of associations via the chi-squared test. 4) Extracts important rules sufficient enough for user's purpose in a short time. 5) The pool is updated in every generation and only important association rules with higher chi-squared value are stored when the identical rules are stored. We have evaluated the proposed method by doing two simulations. Simulation 1 uses fixed threshold values; that is, they remain fixed at initial thresholds during evolution. In simulation 2,thresholds are evolved by mutation in every generation. Fig. 1 shows the number of rules extracted in the pool in simulation 2. It is found that the number of rules extracted has been increased, which means simulation 2 outperforms simulation 1.

An efficient algorithm for important class association rule mining using genetic network programming (GNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. Instead of generating a large number of candidate rules, the method can obtain a sufficient number of important association rules for classification. The proposed method measures the significance of the association via the chi-squared test. Therefore, all the extracted important rules can be used for classification directly. In addition, the method suits class association rule mining from dense databases, where many frequently occurring items are found in each tuple. Users can define conditions of extracting important class association rules. In this paper, we describe an algorithm for class association rule mining with chi-squared test using GNP and present a classifier using these extracted rules.