Research And Applications Of GEP In Polynomial Factorization And Parallel Function Mining

In scientific research people are eager to discover the rules implied in the data. Therefore, proposing a high efficiency and exact way to execute function mining is also a research emphasis in data mining. This paper focuses on the function mining by Gene Expression Programming (GEP), proposing an approach to factorize polynomial functions, GPF (GEP Polynomial Factorization). Consequently, on the basis of GPF, an approach of parallel function mining based on polygene chromosome is also implemented. The main work of this paper includes: 1. Introduces the concept, work flow and the classification of data mining. Analyzes the characteristic of function mining. Proposes the significance of polynomial function factorization and the multiple function expression mining. 2. Proposes the GPF (GEP Polynomial Factorization) algorithm based on GEP (Gene Expression Programming) techniques in spite of the limits of traditional factorization methods to implement polynomial function factorization. 3. Optimize the fitness function in GEP by a truly original approach called probability correlation factor, improving the precision by 27%. And adopt a brand new strategy named LEE(Loose Environment Evolution )to improve the success-probability by 58 times compared with traditional approaches. 4. By extending the former GPF method proposes the Parallel Polynomial function Mining based on polygene chromosome on observation data set, named PPM. 5. Design and complete the experiment platform, named GEPM, based on GEP by using of Visual C++ 6.0, on which both the GPF and PPM function can be implemented. 6. Execute a serial of extensive experiment on GEPM. Compare and evaluate the performance of GPF in different input parameters by designed experiment criterion. Throw out the performance promotion by using the corresponding optimizing strategies. Demonstrate the actual efficiency of PPM. This article is organized as following: Chapter1 talks about the significance of function mining. Chapter2 introduces the concept, work flow of data mining especially function mining. Chapter3 analyzes and compares traditional gene algorithm and gene expression programming, proposes the approach and wok flow of polynomial factorization and parallel function mining. Chapter4 designs and proposes detail steps of GEP polynomial factorization. Chapter5 presents method and steps of parallel polynomial function mining by extending GPF. Chapter6 designs the specific GPF and PPM algorithm and implement the GEPM system containing the tow functions. Chapter7 executes experiments and analyses the results. Chapter8 concludes the work and research of this paper.