Titlebook: Data Mining and Big Data; 7th International Co Ying Tan,Yuhui Shi Conference proceedings 2022 The Editor(s) (if applicable) and The Author(

調(diào)色板

意見一致

Heretical

網(wǎng)絡(luò)添麻煩

https://doi.org/10.1057/9781137297242in then the regression-line does not rotate by the same angle . except for the special case when all .’s are collinear. This makes the regression-line unsuitable as a linear model of a set of data points for applications in data mining and machine learning. We present an alternative linear model tha

維持

ovation

https://doi.org/10.1057/9781137297242erging trends in this field. Besides scientific outputs evaluation using statistical analysis and comparative analysis, scientometric methods such as co-occurrence analysis, cocitation analysis, and coupling analysis were used to analyze the knowledge structure of Fraud detection. Results showed tha

motor-unit

玩忽職守

TERRA

https://doi.org/10.1007/978-3-319-15129-8. However, the existing studies fail to resolve the contradiction between the required solution accuracy and the number of solutions. In this paper, an improved brain storm optimization (BSO) algorithm based on knowledge learning (KLBSO) is proposed as a solution to the problem. The properties of th

假

https://doi.org/10.1007/978-3-319-14809-0most methods focus on mining the local features from node neighbors, while ignoring non-local features in the stock market. Second, most existing works form the portfolio with the stocks with the highest predicted return, exposed to some risk factors that cause common price movements. To reduce the