标题： 贝叶斯混合模型用于频繁项目集发现 显示英文标题

标题： Bayesian Mixture Models for Frequent Itemset Discovery

摘要： 在二进制事务数据挖掘中，传统的频繁项集挖掘常常产生难以直接解释的结果。 为了克服这个问题，通常使用概率模型来生成更紧凑和明确的结果，尽管会有一些准确性的损失。 近年来，贝叶斯统计被广泛应用于机器学习中概率模型的开发，这些方法有许多优点，包括能够避免过拟合。 在本文中，我们开发了两种具有狄利克雷分布先验和狄利克雷过程（DP）先验的贝叶斯混合模型，以改进之前为事务数据集挖掘开发的非贝叶斯混合模型。 我们使用两种方法实现了这两种混合模型的推断：一种是折叠吉布斯采样方案，另一种是变分近似算法。 在多个基准问题上的实验表明，这两种混合模型都比非贝叶斯混合模型表现更好。 变分算法是两种方法中较快的一种，而吉布斯采样方法则能获得更准确的结果。 狄利克雷过程混合模型可以自动增长到适当的复杂度以实现更好的近似。 一旦模型建立，查询和运行分析将非常快速（通常比Eclat快10倍，如我们在实验部分所示）。 然而，这些方法也显示混合模型低估了频繁项集的概率。 因此，这些模型具有更高的灵敏度但更低的特异性。 显示英文摘要

摘要： In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely used in the development of probability models in machine learning in recent years and these methods have many advantages, including their abilities to avoid overfitting. In this paper, we develop two Bayesian mixture models with the Dirichlet distribution prior and the Dirichlet process (DP) prior to improve the previous non-Bayesian mixture model developed for transaction dataset mining. We implement the inference of both mixture models using two methods: a collapsed Gibbs sampling scheme and a variational approximation algorithm. Experiments in several benchmark problems have shown that both mixture models achieve better performance than a non-Bayesian mixture model. The variational algorithm is the faster of the two approaches while the Gibbs sampling method achieves a more accurate results. The Dirichlet process mixture model can automatically grow to a proper complexity for a better approximation. Once the model is built, it can be very fast to query and run analysis on (typically 10 times faster than Eclat, as we will show in the experiment section). However, these approaches also show that mixture models underestimate the probabilities of frequent itemsets. Consequently, these models have a higher sensitivity but a lower specificity.