Mining data from legacy taxonomic literature and application for sampling spiders of the Teutamus group (Araneae; Liocranidae) in Southeast Asia

A Local Discrete Text Data Mining Method in High-Dimensional Data Space

Aiming at the problems of low accuracy, the long time required, and the large memory consumption of traditional data mining methods, a local discrete text data mining method in high-dimensional data space is proposed. First of all, through the data preparation and preprocessing step, we obtain the minimum data divergence and maximize the data dimension to meet the demand for data in high-dimensional space; second, we use the information gain method to mine the pre-processed discrete text data to establish an objective function to obtain the highest information gain; finally, the objective functions established in data preparation, preprocessing, and mining are combined to form a multi-objective optimization problem to realize local discrete text data mining. The simulation experiment results show that our method effectively reduces the time and improves the accuracy of data mining, where it also consumes less memory, indicating that the multi-objective optimization method can effectively solve multiple problems and effectively improve the data mining effect.