检索结果-内蒙古大学图书馆

Design of data mining algorithm based on rough entropy for us stock market abnormality

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5213-5221页

作者： Wang, Guangtong Miao, Jianchun Shanghai Univ Ctr Global Studies Shanghai Peoples R China Chongqing Univ Coll Econ & Business Adm Chongqing Peoples R China

The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a "barometer" of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market.

关键词： US stock market data mining algorithm outlier detection method rough entropy

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Population economic function model based on data mining algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 2369-2382页

作者： Wu, Guoqiang Li, Qingping Huainan Normal Univ Sch Econ & Management Huainan 232001 Peoples R China

Population structure changes interact with economic development, moderate population and reasonable population structure are important guarantees for sustainable social and economic development. The research ignores the specific impact of the change of population age structure on economic growth, and proposes and establishes a population economic function model based on data mining algorithm. Based on the changes of population structure in Liaoning Province in the past 20 years, Grey correlation analysis method is selected. The analysis shows that there is a close relationship between population structure and economic growth. Based on this research, the econometric method is used to construct a multiple linear regression model to further analyze the specific impact of population structure changes on economic growth. The analysis results show that the total population of urban areas, the total number of employed people in the primary industry, the number of middle school students per 10,000 people, and the total number of employed people in the tertiary industry are the four most significant demographic indicators for the per capita GDP of the study area. There is a significant positive correlation between the total number of employed people in the tertiary industry and per capita GDP and there is a significant negative correlation between the total number of employed people in the primary industry and the number of middle school students per capita and per capita GDP. The impact of other indicators on per capita GDP is not significant. According to the conclusion, countermeasures and suggestions to ease population structure change and promote the coordinated development of population and economy in the study area are put forward.

关键词： Grey correlation analysis method data mining algorithm population economic function model

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Power information network intrusion detection based on data mining algorithm

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JOURNAL OF SUPERCOMPUTING 2020年第7期76卷 5521-5539页

作者： Zuo, Xiaojun Chen, Ze Dong, Limian Chang, Jie Hou, Botao State Grid Hebei Elect Power Supply Co Ltd Elect Power Res Inst Shijiazhuang 050000 Hebei Peoples R China

Intrusion detection technology plays an important role in ensuring information security. This paper briefly describes the intrusion detection technology and its development history. Based on the analysis of power information network structure and its security partition, this paper proposes a power information network intrusion detection framework for the intrusion attack problem of power information network and elaborates the implementation of each module. The association rule analysis algorithm and the association relationship between network data stream features can effectively detect the intrusion behaviour in the power information network. Experiments show that the intrusion detection system can effectively detect the intrusion attacks in the power information network and effectively protect the power information.

关键词： data mining algorithm Power information Network intrusion detection

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Research on data mining algorithm of Meteorological Observation Based on data Quality Control algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 2077-2089页

作者： Qing-Dao-Er-Ji, Ren Li, Na Inner Mongolia Univ Technol Sch Informat Engn Hohhot 010051 Peoples R China Inner Mongolia Univ Technol Sch Sci Hohhot 010051 Peoples R China Inner Mongolia Univ Sch Comp Sci Hohhot Peoples R China

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.

关键词： SVM algorithm BP neural network Meteorological observation Quality control data mining algorithm

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Energy analysis of Internet of things data mining algorithm for smart green communication networks

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COMPUTER COMMUNICATIONS 2020年第0期152卷 223-231页

作者： Du, Ziping Suzhou Ind Pk Inst Serv Outsourcing Informat Engn Coll Suzhou 215123 Jiangsu Peoples R China

With the continuous development of Internet technology and electronic information technology, big data technology and cloud computing technology also rise and develop, and have a positive impact on people's lives. data mining system can deeply mine the value information contained in big data, so as to assist users to solve practical problems and help users to make correct decisions and judgments. This paper presents an energy analysis of data mining algorithm based on cloud platform for Internet of things (IoT). First of all, an improved Apriori algorithm is proposed, which is based on Boolean matrix and sorting index rules. Then Boolean matrix is obtained after scanning the data set and the Boolean matrix is preprocessed to delete the useless transactions and the item set, which are combined with sorting index to produce other item sets, effectively improving the efficiency of frequent item mining, which effectively reduce the memory usage. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm needs human intervention in the global parameter selection, and the process of regional query is complex and the query is easy to lose objects. An improved parameter adaptive and regional query density clustering algorithm is proposed, which can effectively delete the redundant data in the high-level complex data space on the premise of retaining the internal nonlinear structure of the IoT data. The efficiency of clustering is also improved accordingly Finally, the simulation based on cloud platform verifies the effectiveness and superiority of the algorithm.

关键词： Energy analysis Internet of things Cloud platform Hadoop data mining algorithm

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Research on data mining algorithm based on neural network and particle swarm optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 2921-2926页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou Jiangsu Peoples R China

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 data mining. 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 data mining 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 data mining with this algorithm has higher accuracy and stronger convergence.

关键词： Neural network algorithm particle swarm optimization algorithm data mining algorithm

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Financial accounting intelligence management of internet of things enterprises based on data mining algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 5915-5923页

作者： Yao, Lu Shandong Yingcai Univ Dept Econ & Management Jinan 250000 Shandong Peoples R China

With the introduction of the information age, enterprise financial management has been challenged as never before, and the application of Internet of Things (IoT) technology can effectively improve the efficiency of financial accounting management and realize the informationization of financial management. In order to solve the problem of enterprise financial accounting data processing, a data mining algorithm is constructed, which uses data mining technology to obtain massive information data and cluster analysis processing to realize the fusion of multiple uncertainty information processing models. Firstly, the financial information cloud platform is designed by using the IoT technology. The financial risk index coefficient of the enterprise is judged by the association rules. Finally, the research sample is divided into the risk group and the normal group according to the ST classification standard, and the 296 financial indicators of the two groups are correlated. The research results show that if the enterprise with a score below 40 points has financial risk, the accuracy rate is 70.9%, which is slightly lower than the financial risk warning model of the decision tree. Through the research of this paper, it has enlightenment to the financial accounting management of IoT enterprises. The data mining technology is applied in the processing of massive data information of accounting, which is more efficient.

关键词： data mining algorithm enterprise financial accounting smart management

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Intelligent manufacturing system based on data mining algorithm

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2021年第4期12卷 396-405页

作者： Liu, Xiaoya Zhou, Qiongjie Xinyang Vocat & Tech Coll Sch Business Xinyang 464000 Henan Peoples R China Wuhan Donghu Univ Sch Management Wuhan 430212 Hubei Peoples R China

This article focuses on the evaluation model of intelligent manufacturing system based on data mining algorithm. Combining data mining algorithm with intelligent manufacturing system, the evaluation model of intelligent manufacturing system is established successfully. According to the characteristics of intelligent manufacturing system, the data is divided into training set, cross-validation set and test set, and then the training set is used to perform neural network training, adjustment and optimisation and verification set. The experiment found that the accuracy rate of the training group was higher than that of the test group, the highest accuracy rate of the training group was 69%, and the highest accuracy rate of the test group was 32.5%. The results show that using data mining algorithms for recognition can effectively cluster control chart patterns and improve recognition efficiency.

关键词： data mining algorithm intelligent manufacturing system evaluation model error analysis

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Wireless Network Health Information Retrieval Method Based on data mining algorithm

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2023年第2期19卷 211-218页

作者： Guo, Xiaoguang Univ Sci & Technol Beijing The Library Tianjin Coll Tianjin Peoples R China

In order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid health information stored in wireless network is filtered by data mapping, and the health information is clustered by data mining algorithm. On this basis, the high-frequency words of health information are classified to realize wireless network health information retrieval. The experimental results show that exactitude of design way is significantly higher than that of the traditional method, which can solve the problem of low accuracy of the traditional wireless network health information retrieval method.

关键词： data mining algorithm Health Information Retrieval Method Wireless Network

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Massive data mining algorithm for Web Text Based on Clustering algorithm

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019年第2期23卷 362-365页

作者： Luo, Nan-Chao Aba Teachers Univ Sch Math & Comp Sci Wenchuan 623002 Sichuan Peoples R China

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 data mining. Experimental results show that the proposed algorithm can effectively improve data mining accuracy and time consuming.

关键词： clustering algorithm Web text massive data data mining algorithm

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