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The ultimate goal of listed companies is to maximize shareholders' wealth. With the increasingly fierce market competition, enterprise managers are constantly exploring the key indicators that have an important impact on the financial performance (FP) of enterprises, and achieve the expected FP of shareholders by improving these key indicators. On the basis of the existing enterprise performance measurement system and index research, through expert scoring to determine the secondary indicators, this paper selects 87 small and medium-sized board listed companies which officially announced the implementation of equity incentive from 2009 to 2012 as the sample, takes the financial information disclosed in 2013 as the empirical data, and analyzes the traditional multiple linear regression analysis (MLR) When dealing with big data, especially the data with hierarchical structure, this paper proposes a partial regression coefficient calculation model based on hierarchical data, constructs a multiple nonlinear regression model, and concludes through empirical analysis that there is a nonlinear correlation between equity incentive ratio and corporate performance, and that there is an interval effect between equity incentive ratio and corporate performance. We also present Fuzzy based financial performance prediction of listed companies. Finally, we demonstrate Comparative analysis for financial prediction in term of accuracy between multiple regression model and fuzzy logic system and result deduce that fuzzy logic gives better accuracy than regression model. [ABSTRACT FROM AUTHOR]

At present, China has great difficulty in obtaining the reliability of teaching data sources. In order to further improve the effectiveness of data mining and reduce the difficulty of data acquisition, this paper studies the design and simulation of integrated education information teaching system based on fuzzy logic. Bayesian algorithm can perform data mining, feature recognition and classification on data in big data, so that it can effectively process massive data sources. By weighting the different network structures, the number of undirected edges in the network is reduced, and then small data sets that can be processed by multiple traditional algorithms are sampled from the big data set, and data is generated by using the Bayesian network toolkit Samiam. The modules respectively generate data sets of different sizes and construct a teaching data source generation model. The experimental results show that RSEM on Child and Alarm data can take less time and achieve an accuracy of 86.17% compared with the whole data set under the same effect. This paper proposes a Bayesian network structure integration model, which can solve the problem of data acquisition difficulties, and is also a further improvement of data mining technology. [ABSTRACT FROM AUTHOR]

Data mining and machine learning methods have been utilized successfully in the past for identifying and forecasting meaningful patterns from data repositories of diverse application domains. However, the high number of dimensions and instances present in large datasets pose great technical challenges to these existing methods of classification and prediction. The presence of noisy data and missing values makes it even tougher to achieve accurate prediction outcomes. A number of hybrid methodologies constituting dimensionality reduction, feature selection and noise removal methods have been proposed in the literature. However, majority of these techniques force the analysts to compromise on accuracy of classification and prediction results. Therefore, there is a strong need of a methodology that not only scales well with the sheer size and volume of data but also provides near to accurate classification and prediction results by effectively handling the noise in data variables. This paper proposes a fuzzy-based methodology which ranks the dimensions in order of importance and exploits Fuzzy Nearest Neighbor (FNN) approaches for accurate classification and prediction. An experimental evaluation on real world datasets, taken from UCI machine learning repository, shows that the proposed approach outperforms the existing classification and prediction methods by employing only a subset of important features to achieve high prediction accuracy rates at multiple levels of data abstraction. [ABSTRACT FROM AUTHOR]