Agrawal和Swami首先提出了基于关联规则的算法的文献
时间: 2024-06-06 17:07:15 浏览: 25
Agrawal, R., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 207-216.
相关问题
关联规则挖掘ARM算法
关联规则挖掘中的ARM算法是指Apriori算法。Apriori算法是一种用于关联规则挖掘的经典算法,主要用于发现频繁项集以及基于这些项集生成关联规则。该算法由Agrawal和Srikant于1994年提出,主要应用于购物篮分析等场景。Apriori算法的核心思想是通过逐步构建候选项集并利用先验知识剪枝,从而减少计算量。具体而言,Apriori算法通过扫描数据集来发现频繁项集,然后基于这些频繁项集生成关联规则。关联规则生成的过程是通过对每个频繁项集产生所有可能的规则组合,并计算这些规则的置信度。最后,根据给定的最小置信度阈值筛选出有效的关联规则。关联规则通常表示为“X -> Y”,其中X是规则的前件,Y是规则的后件。为了评估关联规则的质量,我们使用置信度和提升度这两个指标。置信度表示规则前件出现时规则后件同时出现的概率,而提升度表示规则前件和后件的关联程度。因此,ARM算法即Apriori算法,是一种用于关联规则挖掘的重要算法。\[1\]\[2\]\[3\]
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- *1* *2* *3* [数据挖掘实验-week8-关联规则挖掘(Association Rule Mining)](https://blog.csdn.net/qq_37397652/article/details/130378987)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
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给我一些ID3算法和c4.5算法的参考文献
以下是一些ID3算法和C4.5算法的参考文献:
1. J. R. Quinlan. "Induction of Decision Trees." Machine Learning, vol.1, no.1, pp.81-106, 1986.
2. J. R. Quinlan. "C4.5: Programs for Machine Learning." Morgan Kaufmann Publishers, 1993.
3. J. Han and M. Kamber. "Data Mining: Concepts and Techniques." Morgan Kaufmann Publishers, 2000.
4. I. H. Witten and E. Frank. "Data Mining: Practical Machine Learning Tools and Techniques." Morgan Kaufmann Publishers, 2005.
5. R. Agrawal, T. Imielinski, and A. Swami. "Mining Association Rules between Sets of Items in Large Databases." Proceedings of the ACM SIGMOD Conference on Management of Data, pp.207-216, 1993.
6. C. Zhang, Q. Yao, G. Sun, and T. Zhou. "Review on Decision Tree Algorithms for Classification." Journal of Software Engineering and Applications, vol.7, pp.663-671, 2014.
7. T. M. Mitchell. "Machine Learning." McGraw-Hill, 1997.
8. A. L. Blum and P. Langley. "Selection of Relevant Features and Examples in Machine Learning." Artificial Intelligence, vol.97, pp.245-271, 1997.
9. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. "Classification and Regression Trees." Wadsworth International Group, 1984.
10. C. Bishop. "Pattern Recognition and Machine Learning." Springer-Verlag, 2006.
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