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

Abstract

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.

Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model

“…The above-detailed knowledge intensifies the requirement of discretizing data of the conditional features for financial applications. (4) The study results on data-discretization performance are matched and affirmed with the literature [68]. (5) Furthermore, the key data-discretization methods have been used as a useful tool for defining purposeful linguistic terms in natural language for manipulating rule-based knowledge representation.…”

“…(3) The mean of highest average accuracy, 87.53%, occurred in Models D and E with the cross-validation method in two classes. (4) The highest accuracy of 92.46% occurred in Model C using decision tree learning-C4.5 with the percentage-split method and no time lag in two classes. It is feasible, due to the methodology used in this study, that the classifiers learned an acceptable classification accuracy rate and achieved an adequate ranking in the related financial characteristics applied.…”

“…Commodity risk is highly correlated with natural hazards; this allows it to be analyzed and managed with intelligent information from data mining [2,3]. Some advances in data mining technology (such as by Wang and Miao [4]) applied in stock analysis can definitely assist in escaping risk and achieving better investment performance. Thus, effective analytical tools or techniques are a priority to achieve the goals stated above.…”