Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data

With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.

Consumers’ Purchase Behavior Preference in E-Commerce Platform Based on Data Mining Algorithm

E-commerce platform can recommend products to users by analyzing consumers’ purchase behavior preference. In the clustering process, the existing methods of purchasing behavior preference analysis are easy to fall into the local optimal problem, which makes the results of preference analysis inaccurate. Therefore, this paper proposes a method of consumer purchasing behavior preference analysis on e-commerce platform based on data mining algorithm. Create e-commerce platform user portrait template with consumer data records, select attribute variables and set value range. This paper uses data mining algorithm to extract the purchase behavior characteristics of user portrait template, takes the characteristics as the clustering analysis object, designs the clustering algorithm of consumer purchase behavior, and grasps the common points of group behavior. On this basis, the model of consumer purchase behavior preference is established to predict and evaluate the behavior preference. The experimental results show that the accuracy rate of this method is 91.74%, the recall rate is 88.67%, and the F1 value is 90.17%, which are higher than the existing methods, and can provide consumers with more satisfactory product information push.

Application of Data Mining in Effect Evaluation of Lean Management

Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

Data Mining and Deep Learning for Predicting the Displacement of “Step-Like” Landslides

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R2 values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

Data Mining Algorithm for Moving Object Trajectory

Aiming at the poor effect and long recognition time of data mining algorithm for moving target trajectory recognition, a data mining algorithm based on improved Hausdorff distance is proposed. The position and angle of abnormal trajectory data are detected by calculating the distance between trajectory classification and sub trajectory line segments, and the trajectory unit is established by using the improved Hausdorff distance algorithm to optimize the similarity matching structure. Experimental results show that the algorithm has low error pruning rate in identifying moving target trajectory, improves the detection efficiency of moving target trajectory recognition data, and ensures the quality of moving target trajectory recognition data mining

Research on Artificial Intelligence Classification and Statistical Methods of Financial Data in Smart Cities

In order to improve the effect of financial data classification and extract effective information from financial data, this paper improves the data mining algorithm, uses linear combination of principal components to represent missing variables, and performs dimensionality reduction processing on multidimensional data. In order to achieve the standardization of sample data, this paper standardizes the data and combines statistical methods to build an intelligent financial data processing model. In addition, starting from the actual situation, this paper proposes the artificial intelligence classification and statistical methods of financial data in smart cities and designs data simulation experiments to conduct experimental analysis on the methods proposed in this paper. From the experimental results, the artificial intelligence classification and statistical method of financial data in smart cities proposed in this paper can play an important role in the statistical analysis of financial data.

Smart Financial Management System Based on Data Ming and Man-Machine Management

To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and summary of the algorithm, and an improved algorithm is proposed to address the flaws. Related mining experiments are carried out on the improved algorithm, and the experiment shows that it has certain advantages. Then, using an intelligent forecasting financial management decision as an example, the intelligent financial management based on data mining is thoroughly investigated, the basic design framework for intelligent financial management is established, and the application of a data mining model in decision support system is introduced.

Evaluation Method of IT English Blended Teaching Quality Based on the Data Mining Algorithm

As the mixed education model gradually becomes widespread in various universities in Japan, the evaluation of the quality of IT English mixed education has become a very important issue, and it is worth considering the corresponding evaluation method. In this paper, we use a data mining algorithm to implement an evaluation method for the interconversion of quantitative data and qualitative concepts and use the IT English mixed teaching model to evaluate and analyze the teaching quality of the course. The evaluation method is feasible and provides a mixing method. Evaluation of the quality of education. Reference method.

DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE)

This study uses the C4.5 classification algorithm to determine creditworthness, clasification aims to divide the assigned object intoin a number of categories called classes. In this study, the authorusing data mining and C4.5 algorithm as the selection method. The criteria used are loan installments, prospective customer income, termloan time, status of prospective customers. This study resulted in a classification modeldecision tree using the C4.5 algorithm is included in the Excellent category Classification with an accuracy value of 98.33% and a classification error of 1.67%,so that this study uses 70% training data and 30% test data. From resultthe calculation obtained shows that the C4.5 algorithm can be usedto determine the feasibility of granting credit to Koperasi Jaya customers Together (KORJABE). Keywords: Analysis, Credit Eligibility, C4 Algorithm, Data Mining, Method Abstrak: Penelitian ini menggunakan metode Algoritma C4.5 klasifikasi untuk menentukan kelayakan kredit, klasifikasi bertujuan untuk membagi objek yang ditetapkan ke dalam satu nomor kategori yang disebut kelas. Dalam penelitian ini, penulis menggunankan data mining dan algoritma C4.5 sebagai metode pemilihannya. Kriteria yang digunakan yaitu , angsuran pinjaman,penghasilan calon nasabah,jangka waktu pinjaman ,status calon nasabah. Penelitian ini menghasillkan model klasifikasi pohon keputusan menggunakan algoritma C4.5 termasuk dalam kategori Excellent Classification dengan nilai akurasi sebesar 98,33% dan klasifikasi eror 1,67%, sehingga penelitian ini kan menggunakan data latih 70% dan data uji 30%. Dari hasil perhitungan yang diperoleh menunjukan bahwa algoritma C4.5 dapat digunakan untuk menen tukan kelayakan pemberian kredit kepada nasabah Koperasi Jaya Bersama (KORJABE). Kata kunci: Algoritma C4.5, Analisis, Data Mining, Kelayakan Kredit, Metode

Application of Data Mining in Determining Social Assistance Recipients With C4.5 Algorithm in Maligas Mountain District

The Gunung Maligas District Office is a government agency tasked with running a government program, namely the Social Assistance Receipt program, to run the social assistance program, many residents complain that they do not receive assistance, while some residents who are considered capable actually get assistance, where each aid program is have different criteria in determining the recipient. Due to the large number of existing aid programs with different criteria in determining the acceptance of the aid program, of course, local government staff will have difficulty in conducting the selection process. So we need a system that is able to help local government staff to more easily determine the recipients of the social assistance. Based on the historical data of beneficiaries, recommendations for the classification of beneficiaries can be made that will assist government staff. Classification can be done using the C4.5 algorithm. In this study, it has parameters, namely, occupation, income, housing conditions and number of dependents. By applying the C4.5 data mining algorithm, it is hoped that it will make it easier and faster for government staff to determine the recipients of social assistance at the Gunung Maligas District Office.