The massive data of Web text has the characteristics of high dimension and sparse spatial distribution, which makes the problems of low mining precision and long time consuming in the process of mining mass data of We...
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The massive data of Web text has the characteristics of high dimension and sparse spatial distribution, which makes the problems of low mining precision and long time consuming in the process of mining mass data of Web text by using the current data mining algorithms. To solve these problems, a massive data mining algorithm of Web text based on clustering algorithm is proposed. By using chi square test, the feature words of massive data are extracted and the set of characteristic words is gotten. Hierarchical clustering of feature sets is made, TF-IDF values of each word in clustering set are calculated, and vector space model is constructed. By introducing fair operation and clone operation on bee colony algorithm, the diversity of vector space models can be improved. For the result of the clustering center, K-means is introduced to extract the local centroid and improve the quality of datamining. Experimental results show that the proposed algorithm can effectively improve datamining accuracy and time consuming.
Financial management is an important link in the financial management of colleges and universities. On the basis of the analysis of the defects in the traditional financial management methods, the financial management...
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ISBN:
(纸本)9781728121659
Financial management is an important link in the financial management of colleges and universities. On the basis of the analysis of the defects in the traditional financial management methods, the financial management theory and the early warning theory of the colleges and universities are combined in this paper. The data mining algorithm is applied to establish the financial management information system model of the colleges and universities. Firstly, A universities in both Shanghai City and Shenzhen City in the recent two years are taken as the research subjects. The colleges and universities that are included in the special treatment due to the abnormal financial situation are defined as the financial crisis sign for the colleges and universities. In addition, the financial data in the annual report of the colleges and universities are adopted as the input feature vector, and then the data mining algorithm is combined with the datamining to establish a kind of financial management information system model of the colleges and universities through the empirical methods. The empirical results have verified the effectiveness of the model put forward in this paper.
The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the el...
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The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the elements and can't meet the needs of the quality control work. Therefore, based on the data mining algorithm, this paper further studied the quality control of meteorological data from two aspects of time correlation and factor correlation. Two different methods of quality control for meteorological observation data were proposed. One is the quality control method of time correlated meteorological observations based on the characteristics of chaos (potential trend and regularity) and the support vector machine algorithm. The other is the quality control method of factor correlated meteorological observations based on BP neural network and the characteristics of different elements. Combining the complementarity and relevance between the two methods, a set of comprehensive quality control scheme is set up. The experimental results show that the proposed scheme can effectively simulate the weather observation data and detect the anomaly value.
In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of datamining. So this time we prop...
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In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of datamining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed datamining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed datamining with this algorithm has higher accuracy and stronger convergence.
In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumula...
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In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream miningalgorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher datamining efficiency and robustness.
With the advent of the information age, the rapid development of computer technology has been widely used in various fields of production and life, and has brought great convenience and progress. In today's educat...
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With the advent of the information age, the rapid development of computer technology has been widely used in various fields of production and life, and has brought great convenience and progress. In today's education field, our country attaches great importance to cultivate all-round talents, which requires all-round development. It not only contribute to the rapid development of sports education in the industry, but also the development of sports promote the level that all the people to attach much importance to sports, so that it becomes an important pillar industry. So how to improve the information level of physical education, strengthen the information construction of physical education is also an essential research field. This paper mainly analyzes sports performance management in physical education and the application of computer technology in performance management, combined with datamining technology and miningalgorithm, carry out a research on performance management in the performance system. It mainly analyze method and characteristic of datamining and the application of data mining algorithm in sports performance management, It gives an example to simulate the application, through the analysis and research, and finally achieve the goal of high quality and efficiency of school in physical education teaching.
To improve the efficiency and safety of the user privacy awareness model in a mobile payment environment, the datamining method and algorithm is proposed in this paper. The author develops a novel architectural frame...
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To improve the efficiency and safety of the user privacy awareness model in a mobile payment environment, the datamining method and algorithm is proposed in this paper. The author develops a novel architectural framework that observes user-specific patterns to distinguish between legitimate users and illegitimate users, thereby enhancing and strengthening user authentication. The algorithm and model use multidimensional receptive-field function and support vector method to improve accuracy and efficiency. The results show that the proposed method can improve the performance of the user privacy awareness model in the mobile payment environment.
In recent years, the rapid development of the Internet and computer-related technologies, including photography, video, e-commerce, etc., so that the data generated around us was the explosive growth, especially after...
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ISBN:
(纸本)9781510835429
In recent years, the rapid development of the Internet and computer-related technologies, including photography, video, e-commerce, etc., so that the data generated around us was the explosive growth, especially after the rise of the smart phone mobile Internet technology as the representative of others obvious. Faced with such a large-scale data analysis and data processing become a huge problem, which would give the opportunity to the development of datamining. datamining can extract valuable information from users of these massive, heterogeneous, random data found interesting user mode.
In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of e...
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In the fiercely competitive realm of sports and physical education, the application of data mining algorithms has emerged as a vital solution. Machine learning has streamlined processes, offering a seamless means of elevating the quality of education and training provided to students, particularly in the context of sports. This technological support empowers the sports education system to make more informed decisions pertaining to the physical development of aspiring athletes. In this comprehensive study, a blended approach of qualitative methods has been leveraged to gather intricate insights, enriching the overall understanding of the subject. Additionally, an in-depth exploration of articles and journals has been undertaken to scrutinize the practical implementation of dataalgorithm techniques geared towards enhancing physical training. The resultant findings underscore a substantial and tangible nexus between dataalgorithms and the domain of sports education. Of paramount significance is the central role played by data mining algorithms in augmenting performance. Notably, the National Sports Board (NSB) has extensively harnessed this technology to meticulously monitor players' on-field performance, ultimately leading to a granular comprehension of each player's capabilities. This paper emphasizes the methods of optimizing mistake detection and its joining systems for increasing the punishment in the operational procedures.
This article proposed a supplier management decision support system based on data mining algorithms, aiming to help enterprises select the best supplier to improve procurement efficiency and reduce procurement costs. ...
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