摘要
贝叶斯方法在数据挖掘领域发挥着关键作用，它通过构建概率模型来揭示数据背后的潜在规律和模式。在文本分类、关联规则挖掘以及异常检测与预测等方面，贝叶斯方法具有显著优势。作为一种基于概率论的统计推断方法，贝叶斯推断在数据挖掘领域得到了广泛应用，有助于我们更深入地理解和分析复杂数据。在文本分类、情感分析、垃圾邮件检测、金融风控等领域，贝叶斯推断展现了其独特优势。展望未来，随着计算能力的提升、算法的优化，以及与深度学习等先进技术的深度融合，贝叶斯推断将继续在数据挖掘领域发挥关键作用，为人工智能的发展和应用带来更多可能性与机遇。
关键词： 贝叶斯方法；数据挖掘；统计学
Abstract
Bayesian methods play a key role in the field of data mining by constructing probabilistic models to reveal the underlying laws and patterns behind the data. Bayesian methods have significant advantages in text classification, association rule mining, and anomaly detection and prediction. As a statistical inference method based on probability theory, Bayesian inference is widely used in the field of data mining, which helps us understand and analyse complex data more deeply. In the fields of text classification, sentiment analysis, spam detection, financial risk control, etc., Bayesian inference shows its unique advantages. Looking ahead, with the improvement of computing power, optimisation of algorithms, and deep integration with advanced technologies such as deep learning, Bayesian inference will continue to play a key role in the field of data mining, bringing more possibilities and opportunities for the development and application of artificial intelligence.
Key words： Bayesian methods; Data mining; Statistics
参考文献 References
[1] Wang, Chunxia, and Xiaoyue Zheng. "Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint." Evolutionary Intelligence (2020).
[2] 张彦斌. 基于贝叶斯理论的结构化数据处理技术研究[D].北京邮电大学,2021.
[3] 程和祥,聂炜昌.人工智能中的贝叶斯方法[J].重庆理工大学学报(社会科学),2020,34(05):17-23.
[4] Vassend, O.. "New Semantics for Bayesian Inference: The Interpretive Problem and Its Solutions." Philosophy of Science (2019).
[5] Bao, Fuguang, et al. "An Improved Evaluation Methodology for Mining Association Rules." Axioms (2021).
[6] 杨哲. 基于自助法的贝叶斯网结构学习[D].长春工业大学,2020.
[7] 杨洋. 贝叶斯优化和关联规则挖掘的若干问题研究[D].清华大学,2020.
[8] 化越. 组合数据下贝叶斯网络构建算法研究[D].北方工业大学,2020.
[9] 郭志高. 小数据集条件下贝叶斯网络参数学习方法研究[D].西北工业大学,2019.
[10] Aqra, Iyad, et al. "Incremental Algorithm for Association Rule Mining under Dynamic Threshold." Applied Sciences (2019).
[11] Jiang, Xia. "Advanced Properties of Bayesian Networks." (2018).
[12] 方红燕,王蕊,杨文志等.贝叶斯公式实例的深度挖掘[J].曲阜师范大学学报(自然科学版),2018,44(04):1-4.
[13] 刘金銮. 基于贝叶斯网络的复杂数据生成方法与技术研究[D].北方工业大学,2018.
[14] 阚裕隆. 基于k-tree优化贝叶斯网络的上位性挖掘方法研究[D].华中农业大学,2021.
[15] Pigliucci, M.. "Bayes’s Theorem." The SAGE Encyclopedia of Research Design (2022).
[16] 李昡熠,周鋆.基于频繁项挖掘的贝叶斯网络结构学习算法BNSL-FIM[J].计算机应用,2021,41(12):3475-3479.
[17] 曹林林,吴洁琪,孙利华.贝叶斯网络在映射法中的应用[J].中国卫生经济,2020,39(04):76-78.
[18] Giummolè, F., et al. "Objective Bayesian inference with proper scoring rules." TEST (2017).