Abstract: The adaptive partitioning algorithm of information set in simulation laboratory based on project-based learning data-driven and random grid is studied to effectively preprocess the information set and improve the adaptive partitioning effect of the information set. Using the improved fuzzy C-means clustering algorithm driven by project-based learning data, the fuzzy partition of information set in simulation laboratory is carried out to complete preprocessing of information set; The pre-processing information set space is roughly divided by the grid partitioning algorithm based on the data histogram; A random mesh generation algorithm based on uniformity is used to finely divide the coarse mesh cells; Taking the representative points of grid cells as the clustering center, the pre-processing information set is clustered by the density peak clustering algorithm to complete the adaptive partitioning of the information set in simulation laboratory. Experimental results show that this algorithm can effectively preprocess and adaptively partition the information set of simulation laboratory; For different dimension information sets, the evaluation index values of Rand index, Purity, standard mutual information, interval and Dunn index of the algorithm are all high, and the evaluation index values of compactness and Davidson's banding index are all low, so the algorithm has a high accuracy of adaptive partitioning of information sets.

Xiaomei Qin and Wenlan Zhang, “Information Classification Algorithm based on Project-based Learning Data-driven and Stochastic Grid” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01309114

@article{Qin2022,
title = {Information Classification Algorithm based on Project-based Learning Data-driven and Stochastic Grid},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01309114},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01309114},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Xiaomei Qin and Wenlan Zhang}
}