FP-growth method is an efficient algorithm to mine frequent patterns, in spite of long or short frequent patterns. By using compact tree structure and partitioning-based, divide-and-conquer searching method, it reduces the search costs substantially. But just as the analysis in Algorithm, in the process of FP- tree construction, it is a strict serial computing process. Algorithm performance is related to the database size, the sum of frequent patterns in the database: ω. this is a serious bottleneck. People may think using distributed parallel computation technique or multi-CPU to solve this problem. But these methods apparently increase the costs for exchanging and combining control information, and the algorithm complexity is also greatly increased, cannot solve this problem efficiently. Even if adopting multi-CPU technique, raising the requirement of hardware, the performance improvement is still limited.

Based on the secant equations proposed by Zhang, Deng and Chen, we propose a new nonlinear conjugate gradient method for unconstrained optimization problems. Global convergence of this method is established under some proper conditions.

Optimization and solving NP-hard problems are very important and Evolutionary Computing (EC) methods are useful and popular. There are different types of EC methods that most of them are sequential and some others have parallel implementation. In first step we want to review some parallel implementation of EC methods and in second step we introduce four general frameworks to parallelize all EC algorithms that they are Master-Slave method, Hybrid method, Simple Multi-population method and Repulsive Multi-Population method. Finally we create a new operation for migration to keep population diversity.

In this paper, we will use the variational iteration method (VIM) for the determination of unknown coefficients in an inverse heat conduction problem (IHCP). The VIM, which is a modified general Lagrange multiplier method, has been attracted a lot of attention of the researchers for solving different problems. Applying this technique, a rapid convergent sequence to the exact solution is produced. Moreover, this method does not require any discretization, linearization or small perturbation. Therefore it can be considered as an efficient method to solve the various kinds of problems. To show the strength of the method, some examples are given.

In this paper, we propose a filter method to solve the linear complementarity problem(LCP). By using the Fisher-Burmeister function, we convert the LCP to an equivalent optimization problem with linear equality constraints. A filter approach is employed to tackle the optimization problem and the proposed mechanism for accepting the trial step is obtained by a nonmonotone filter technique. Under some conditions, we establish the global convergence of the algorithm.

In this paper, a collocation method based on the Bessel polynomials are used for the solution of nonlinear Fredholm-Volterra-Hammerstein integral equations (FVHIEs). This method transforms the nonlinear (FVHIEs) in to matrix equations with the help of Bessel polynomials of the first kind and collocation points. The matrix equations corresponds to a system of nonlinear algebraic equations with the unknown Bessel coefficients. Present results demonstrate proposed method in comparison with other methods is more accurate, efficiency and reliability.

The current paper is concerned with a decision making problem of a tea industry of Barak Valley of Assam, India. The goal of this problem is to maximize the overall profit of the industry subject to the given resource constraints. Firstly, the problem has been formulated as an integer linear programming problem in crisp as well as in fuzzy environment. For fuzzification of the problem, some of the parameters are assumed to be different types of fuzzy numbers viz., triangular fuzzy number, parabolic fuzzy number and trapezoidal fuzzy number. Thereafter, the problem has been defuzzified by graded mean integration method to convert the crisp problem. The converted problem has been solved by the software LINGO13.0. Finally, to illustrate the problem and its results, a numerical example has been solved.

In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification processes.