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The motivation of the paper is to find out the influence of demographic characteristic factors on employees’ career promotion in China’s state-owned enterprises and study the relationship between promotion mechanism and enterprise benefits. More than 6,500 pieces of human resource data from China’s state-owned enterprises were studied. Logistic regression is adopted to analyze the correlation between demographic characteristics and promotion. Meanwhile, different data mining methods are used to summarize the characteristics of promotion. On this basis, this study uses the principal-agent model of game theory to analyze the profits of employees and enterprises under different promotion mechanisms and demonstrates the conditions for enterprises to obtain the maximum benefit from the employees’ promotion. The results provide a theoretical reference for the assessment indicator selection of enterprise promotion and help executives better understand the impact of promotion mechanism on enterprises and employees. The application of results can reduce the information asymmetry in promotion incentive, prevent the emergence of adverse selection, and achieve a win-win situation for enterprises and employees.

Time series is a very popular type of data which exists in many domains. Clustering time series data has a wide range of applications and has attracted researchers from a wide range of discipline. In this paper a novel algorithm for shape based time series clustering is proposed. It can reduce the size of data, improve the efficiency and not reduce the effects by using the principle of complex network. Firstly, one-nearest neighbor network is built based on the similarity of time series objects. In this step, triangle distance is used to measure the similarity. Of the neighbor network each node represents one time series object and each link denotes neighbor relationship between nodes. Secondly, the nodes with high degrees are chosen and used to cluster. In clustering process, dynamic time warping distance function and hierarchical clustering algorithm are applied. Thirdly, some experiments are executed on synthetic and real data. The results show that the proposed algorithm has good performance on efficiency and effectiveness.

Multivariate time series classification is of significance in machine learning area. In this paper, we present a novel time series classification algorithm, which adopts triangle distance function as similarity measure, extracts some meaningful patterns from original data and uses traditional machine learning algorithm to create classifier based on the extracted patterns. During the stage of pattern extraction, Gini function is used to determine the starting position in the original data and the length of each pattern. In order to improve computing efficiency, we also apply sampling method to reduce the searching space of patterns. The common datasets are used to check our algorithm and compare with the naive algorithms. Experimental results are shown to reveal that much improvement can be gained in terms of interpretability, simplicity and accuracy.

In this paper, a kind of modified adaptive group recommender based on overlapping community detection (GROCD) is proposed. Different from existing recommenders, GROCD takes both of group members' preferences and their complex internal interactions into account. In this research, both of overlapping community integration strategy and contribution-based collaborative filtering are employed to explore group members' interests and provide the predicted group ratings on movies. The authors discuss the effectiveness of the proposed approach on Movielens dataset. The results show that the proposed recommender can achieve comparatively accurate prediction with a comparatively low computation complexity.

The current information service platforms often consider service providers and users to be credible, causing that the service quality of platform is not high, but in the actual web, the parties involved in the service may be false and malicious. Thus low degree reputation seriously affects the quality of services and other issues of information service platform. In this paper, based on KMRW, firstly we evaluate the credibility of the information source, and then, using the double-Rating credit evaluation model, we effectively describe the service and feedback trust evolutionary relationship between information service platform and users.

Spectrum is a valuable and scarce natural resource. Regulators assign spectrum usage rights to holders mainly by issuing licenses. With gradual introduction of spectrum trading and liberation, problems of tenure and assignment of spectrum licenses have attracted increasing attention. This paper does not only do lots of empirical research on existing license types in countries that have introduced spectrum trading, but also do in-depth study on the new license type- perpetual license that scholars have proposed. Then, it classifies spectrum licenses into unrenewable licenses, renewable licenses and perpetual licenses according to usage duration and renewal expectancy. Finally, both initial and secondary assignment processes of three kinds of spectrum licenses are put forward.

Traditional collaborative filtering algorithm is a weighted average prediction algorithm based on nearest neighbors' ratings. Besides similarity between users, trust and credit are also parameters to affect recommendation. This paper proposes a computational model of credit factor and then a collaborative filtering algorithm based on it. This model is based on trust factor and takes credit model as the basic elements. This proposed algorithm further improves the validity and accuracy of the recommendation.