Web Reference: Apriori算法的瓶颈 Apriori算法简单，易于实现。 但是它也有自己的缺点，数据集很大的时会出现下面两个问题。 1. 需要多次扫描数据集 2. 可能会产生庞大的候选集 针对Apriori算法的性能瓶颈问题，2000年Jiawei Han等人提出了基于FP树生成频繁项集的FP-growth算法。 在医疗诊断领域，Apriori算法已被应用于多种实际场景，以辅助医生进行更准确的诊断和风险评估。以下是一些具体的应用实例： 康复护理人员的医疗智能系统：一项研究利用Apriori算法来检测医院中的康复护理人员，对医疗数据进行挖掘和探索，设计和构建了一个医疗智能系统。该系统通过疾病分类 ... 这可以通过函数 apriori_gen 来完成，该函数可以使用 Matlab 自带的函数 nchoosek 以及 ismember 来实现。 最后，利用 Apriori 算法生成频繁项集，并计算关联规则，并选择 minConf 使用 MATLAB 自带的函数 confidence 来实现算法。
YouTube Excerpt: Machine Learning Course (Use Code "YOUTUBE20"): https://www.edureka.co/machine-learning-certification-training This ...

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Apriori Algorithm Tutorial For Beginners | Association Rule Mining | Edureka | DS Rewind - 1

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