The exiting algorithms generate a lot of utility pattern trees based on memory when mining high utility patterns in large-scale database, leading to occupying more memory spaces and losing some high utility itemsets. Using Hadoop platform, a parallel high utility pattern mining algorithm, named PUCP, based on cluster partition was proposed. Firstly, the clustering method was introduced to divide the transaction database into several sub-datasets. Secondly, sub-datasets were allocated to each node of Hadoop to construct utility pattern tree. Finally, the conditional pattern bases of the same item which generated from utility pattern trees were allocated to the same node, reducing the crossover operation times of each node. The theoretical analysis and experimental results show that, compared with the mainstream serial high utility pattern mining algorithm named UP-Growth (Utility Pattern Growth) and parallel high utility pattern mining algorithm named HUI-Growth (Parallel mining High Utility Itemsets by pattern-Growth), the mining efficiency of PUCP is increased by 61.2% and 16.6% respectively without affecting the reliability of the mining results; and the memory pressure of large data mining can be effectively relieved by using Hadoop platform.

Many approaches which are developed to compute structural balance degree of signed networks only focus on the balance information of local network without considering the balance of network in larger scale and even from the whole viewpoint, which can't discover unbalanced links in the network. In order to solve the problem, a method of computing global unbalanced degree of signed networks based on culture algorithm was proposed. The computation of unbalanced degree was converted to an optimization problem by using the Ising spin glass model to describe the global state of signed network. A new cultural algorithm with double evolution structures named Culture Algorithm for Signed Network Balance (CA-SNB) was presented to solve the optimization problem. Firstly, the genetic algorithm was used to optimize the population space. Secondly, the better individuals were recorded in belief space and the situation knowledges were summarized by using greedy strategy. Finally, the situation knowledge was used to guide population space evolution. The convergence rate of CA-SNB was improved on the basis of population diversity. The experimental results show that, the CA-SNB can converge to the optimal solution faster and can be more robust than genetic algorithm and matrix transformation algorithm. The proposed algorithm can compute the global unbalanced degree and discover unbalanced links at the same time.

It is necessary to pre-define a cluster number in classical Fuzzy C-means (FCM) algorithm. Otherwise, FCM algorithm can not work normally, which limits the applications of this algorithm. Aiming at the problem of pre-assigning cluster number for FCM algorithm, a new fuzzy cluster validity index was presented. Firstly, the membership matrix was got by running the FCM algorithm. Secondly, the intra class compactness and the inter class overlap were computed by the membership matrix. Finally, a new cluster validity index was defined by using the intra class compactness and the inter class overlap. The proposal overcomes the shortcomings of FCM that the cluster number must be pre-assigned. The optimal cluster number can be effectively found by the proposed index. The experimental results on artificial and real data sets show the validity of the proposed index. It also can be seen that the optimal cluster number are obtained for three different fuzzy factor values of 1.8, 2.0 and 2.2 which are general used in FCM algorithm.