University Students' Behavior Characteristics Analysis and Prediction Method Based on Combined Data Mining Model

2020 ◽  
Author(s):  
Yuansheng Li ◽  
Daojiang Li
Keyword(s):  
Data Mining ◽  
University Students ◽  
Prediction Method ◽  
Characteristics Analysis ◽  
Behavior Characteristics ◽  
Mining Model ◽  
Combined Data

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

Research on Key Technology of Data Mining for Volleyball Game Based on Service System

2014 ◽  
Vol 543-547 ◽  
pp. 4698-4701
Author(s):  
Juan Wang
Keyword(s):  
Data Mining ◽  
High Precision ◽  
Service System ◽  
Processing System ◽  
Machining Process ◽  
Precision Machining ◽  
Intelligent Processing ◽  
Mining Model ◽  
Processing Cycle ◽  
Key Parameter

During the processing of aircraft and other high precision machinery workpieces, if using the traditional machining methods, it will consume a amount of machining costs, and the mechanical processing cycle is long. In this context, this paper designs a kind of robot intelligent processing system with high precision machinery. And it has realized the intelligent online control on the machining process by using the high precision machining intelligent online monitoring technology and the numerical simulation prediction technology. Finally, this system is introduced into the process of data mining for volleyball game, and designs the partial differential variational data mining model, which has realized the key parameter data mining of volleyball games service system, and has provided reliable parameters and technical support for the training of volleyball players.

scholarly journals Data Transformation Techniques for Academic Datasets

2019 ◽  
Vol 9 (1) ◽  
pp. 2214-2218
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Data Preprocessing ◽  
Data Normalization ◽  
Multi Layer Perceptron ◽  
Data Preparation ◽  
Transformation Techniques ◽  
Development Life Cycle ◽  
Heterogeneous Datasets ◽  
Mining Model

Data mining is a real-world procedure of discovering useful patterns from heterogeneous datasets. All most all industry uses data mining in their day to day activities. To build an effective mining model, a series of development steps are to be followed. It starts with discovering the business problem and ends with communicating the results. In this development life cycle, the most important step is data preparation or data preprocessing. Data preprocessing is converting raw data into data understandable by the machine. Data normalization is a phase in data preprocessing where the data values are scaled to 0 and 1. Right normalization of the datasets leads to improved mining results. In this paper, academic data of students is taken. The dataset is normalization using six normalization technique. Multi Layer Perceptron classifier is applied to normalized dataset and results are obtained. Results of this study reveal the best normalization technique which can be used for normalizing academic datasets. Finally, in a line, the goal of this work is to discover the best normalization technique which produces better mining result when applied to academic datasets.

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