Guangfei Yang

A personalized association rule ranking method based on semantic similarity and evolutionary computation

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.

Mining equalized association rules from multi concept layers of ontology using Genetic Network Programming

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.

An evolutionary approach to rank class association rules with feedback mechanism

In this paper, we propose an evolutionary associative classification method by considering both adjustment of the order of the whole set of rules and refinement of the power of each single rule. We discover an interesting phenomenon that the classification performance could be improved if we import some prior-knowledge to re-rank the association rules, where the prior-knowledge could be some equations generated by combing the support and confidence values with various functions. We make use of Genetic Network Programming to automatically search the equation space for prior-knowledge. In addition to rank the rules by equations globally, we also develop a feedback mechanism to adjust the rules locally, by giving some rewards to good rules and penalties to bad ones. Because the proposed method is based on evolutionary computation, we could gradually refine the power of each rule so that it could affect the classification results more precisely. The experimental results on UCI benchmark datasets show that the proposed method could improve the classification accuracies effectively.

A novel evolutionary method to search interesting association rules by keywords

In this paper, we propose an evolutionary method for directly mining interesting association rules. Most of the association rule mining methods give a large number of rules, and it is difficult for human beings to deal with them. We study this problem by borrowing the style of search engine, that is, searching association rules by keywords. Whether a rule is interesting or not is decided by its relation to the keywords, and we introduce both semantic and statistical methods to measure such relation. The mining process is built on an evolutionary approach, Genetic Network Programming (GNP). Different from the conventional GNP based association rule mining method, the proposed method pays more attention to generate the GNP individuals carefully, which will mine interesting association rules efficiently. After the rules are generated, they will be ranked and annotated by meaningful information, such as similar rules and representative transactions, in order to help the user to understand the rules better. We also discuss how to mine generalized interesting association rules, describing more abstract level of information than the common association rules. In the simulation section, we give some demonstrations of the proposed method using a census data set, which shows a promising way to find the interesting association rules.

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

In this paper, we propose a genetic network programming based method to mine generalized association rules with 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 genetic network programming (GNP) to mine the rules. Two kinds of fitness functions each with four kinds of policies and a new genetic operator are developed to speed up searching the rule space.

Evolving directed graphs with artificial bee colony algorithm

Artificial bee colony (ABC) algorithm is a relatively new optimization technique that simulates the intelligent foraging behavior of honey bee swarms. It has been applied to several optimization domains to show its efficient evolution ability. In this paper, ABC algorithm is applied for the first time to evolve a directed graph chromosome structure, which derived from a recent graph-based evolutionary algorithm called genetic network programming (GNP). Consequently, it is explored to new application domains which can be efficiently modeled by the directed graph of GNP. In this work, a problem of controlling the agentss behavior under a wellknown benchmark testbed called Tileworld are solved using the ABC-based evolution strategy. Its performance is compared with several very well-known methods for evolving computer programs, including standard GNP with crossover/mutation, genetic programming (GP) and reinforcement learning (RL).

Ranking association rules for classification based on genetic network programming

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.

A System for Mining Generalized Association Rules with Ontology Using Genetic Network Programming

In this paper, we propose a genetic network programming based system to mine equalized association rules in multi concept layers of ontology. There are three modules in this system: database, equalizer and GNP miner. We first introduce ontology to facilitate building the multi concept layers in Database module and propose dynamic threshold approach (DTA) to equalize the different layers in Equalizer. We make use of an evolutionary computation method genetic network programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space.

A genetic network programming based method to mine generalized association rules with ontology

In this paper, we propose a genetic network programming based method to mine generalized association rules with 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 genetic network programming (GNP) to mine the rules. Two kinds of fitness functions each with four kinds of policies and a new genetic operator are developed to speed up searching the rule space.

Searching interesting association rules based on evolutionary computation

In this paper, we propose an evolutionary method to search interesting association rules. Most of the association rule mining methods give a large number of rules, and it is difficult for human beings to deal with them. We study this problem by borrowing the style of a search engine, that is, searching association rules by keywords. Whether a rule is interesting or not is decided by its relation to the keywords, and we introduce both semantic and statistical methods to measure such relation. The mining process is built on an evolutionary approach, Genetic Network Programming (GNP). Different from the conventional GNP based association rule mining method, the proposed method pays more attention to generate the GNP individuals carefully, which will mine interesting association rules efficiently.