A Correlation Analysis Model of Human Factors in Mine Accidents Based on Apriori Algorithm

Abstract

Human factors are the main cause of mine accidents. With the aid of the Apriori algorithm, this paper establishes a new correlation analysis model for human factors in mine accidents. The model can predict the failure probability of human factors. Next, an evaluation index system for human reliability of mine operators was constructed. On this basis, fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) were combined with neural network (NN) to evaluate human reliability of mine operators with stable performance. Meanwhile, principal component analysis (PCA) was combined with NN to evaluate human reliability of mine operators with unstable performance. Compared with the traditional evaluation methods, the proposed model reduces the dimension of input vector set, tolerates incomplete data samples, and achieves a high prediction accuracy.

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

“…The Apriori algorithm aims to mine association rules with the help of frequent itemsets [20][21][22][23][24]. The basic idea of the algorithm is: first, find all frequent itemsets, whose support is greater than or equal to the predefined minimum support; then, identify the strong association rules from the frequent itemsets, which must satisfy both minimum support and minimum confidence; next, generate the rules that only contain the items in the corresponding set; after that, retain only the rules whose confidence is greater than the user-defined minimum confidence.…”