This paper expounds the application and development prospects of the visualization technology in data mining.

Overview of Visualization Technology in Data Mining

This paper takes the time deposit order data of banking institutions as the research object. Firstly, the data of banking institutions are analyzed and cleaned to convert the categorical data into numeric data. Then, to "split" preprocessing the data after processing, divided into training set and test set. In the training set, LR, Naive Bayes, K-NN, Decision Tree, Random Forests, Extra - trees, AdaBoost, GBDT, XGBoost, LightGBM and CatBoost algorithm (via the grid search method GridSearchCV) are established to study the customer's deposit ordering tendency respectively. Finally, the Stacking fusion algorithm in Ensemble Learning(The meta-learner adopts LR algorithm) is used to fuse each single model to build the best bank customer time deposit ordering classification model. The research results show that using python, based on the different dimensions of bank customer information, the Stacking fusion algorithm based on Ensemble algorithm has better prediction effect than the single model, and the Stacking fusion algorithm has better robustness. Therefore, this paper takes the Stacking fusion model as the final prediction model to effectively identify bank customers and help banking institutions understand customer subscription tendency.

Research on Customer Deposit Order Based on Data Mining Technology

Based on the development of big data era, the processing speed of various information data is accelerated. From the current social information data processing perspective, the traditional information data processing algorithm is no longer applicable to some large-scale data. The application of data mining technology in all walks of life has a very obvious effect. Machine learning algorithm can use artificial intelligence technology, use a large number of data samples to practice, and then automatically find the operation parameters and models. Based on the past achievements, this paper proposes a three-stage positioning method based on support vector machine and K- nearest neighbor method, which greatly improves the positioning accuracy and speed compared with the previous positioning methods based on machine learning. The actual simulation results show that the proposed scheme combines SVM-based positioning method and K- nearest neighbor method, and makes a compromise between positioning accuracy and time complexity, and achieves good positioning results.

Practice of Machine Learning Algorithm in Data Mining Field

Now use of data mining technology to extract effective information become the hotspot of networkThe introduction of data mining process and classification,and putting forward ourselves ideas of data collection and storage in the cloudsFocus on grid and fractal dimension clustering algorithm(GFDC) of data collection,as well as the relationship between the Master and server in the network of frequent data migration improved

Data Mining Technology Research Based on Cloud Computing

Moral judgements on human and autonomous drivers’ decisions in unavoidable collisions scenarios

Machine learning is a multi-disciplinary subject that has emerged over the past 20 years and involves many disciplines such as probability theory, statistics, approximation theory, analytical theory, and computational complexity theory. Machine learning theory is primarily about designing and analyzing algorithms that allow computers to automatically “learn”. Machine learning algorithms are a class of algorithms that automatically analyze and obtain rules from data and use rules to predict unknown data. Because learning algorithms involve a large number of statistical theories, machine learning is particularly closely related to inferred statistics, also known as statistical learning theory. In terms of algorithm design, machine learning theory focuses on achievable, effective learning algorithms. Many inference problems are difficult to follow without program, so part of the machine learning research is to develop an approximation algorithm that is easy to handle.

https://webofproceedings.org/proceedings_series/ESR/ISRME 2019/ISRME19074.pdf

The Application of Machine Learning in Data Mining

Ethical issues surrounding autonomous vehicles (AVs) have emerged as a classic topic in academic circles. When an AV faces a situation with inevitable casualties, how can the machine make the most ethical decision about who to sacrifice? Does the AV protect its passengers in the AV, or save pedestrians on the roadway? To provide reference for designing moral algorithms that govern AVs, the present study considers the roles of time pressure, pedestrian demographics, and driver demographics on human driver’s moral decision-making. Sixty college students (50% men, mean age = 20.13 years, SD = 1.76) participated in a series of moral decisions involving self- versus other-sacrifice within a virtual reality driving scenario. A 3 (pedestrian age group: child vs middle-age vs elderly) x 2 (pedestrian gender: male vs female) x 2 (time pressure: present vs absent) x 2 (participant gender: male vs female) mixed factorial design was implemented. The results showed that: (1) individuals chose to sacrifice themselves more often than pedestrians, with self-sacrifice more common when faced with child pedestrians and female pedestrians; (2) female participants chose to sacrifice themselves more often than males; and (3) when facing time pressure, individuals chose to sacrifice themselves more than pedestrians, but there was no significant difference between self vs other-sacrifice without time pressure.We concluded that moral decisions when faced with inevitable sacrifice in a simulated traffic situation followed super altruism patterns. Female participants were more deontological in moral judgment and greater priority was given to saving female and child pedestrians in decisions. Further, individual’s decision making under time pressure was more deontic. Results have implications for development of AV machine learning paradigms. 

Who Should We Choose to Sacrifice, Self or Pedestrian? Evaluating Moral Decision-Making in Virtual Reality

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The Application of Machine Learning in Data Mining

Who Should We Choose to Sacrifice, Self or Pedestrian? Evaluating Moral Decision-Making in Virtual Reality