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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/association-rules-analysis
课程评论：没有评论
课程名称：关联规则分析 课程概述： “关联规则与离群点分析”课程旨在向学生介绍无监督学习方法的基本概念，特别关注关联规则和离群点检测。参与者将深入研究频繁模式和关联规则，了解Apriori算法和基于约束的关联规则挖掘。此外，学生还将探索离群点检测方法，全面理解上下文离群点。通过互动教程和实践案例，学生将获得实际应用的技能。 课程大纲： 1. **频繁项集**： 本周介绍无监督学习和关联规则分析。您将探索频繁项集，理解其在发现交易数据模式中的重要性。同时，学习关联规则的关键指标，如支持度、置信度和提升度，这些都是衡量关联规则质量的重要指标。 2. **关联规则挖掘**： 本周我们将简要讨论关联规则挖掘，包括闭合模式和最大模式。 3. **Apriori 和 FP Growth 算法**： 本周重点关注Apriori和FP Growth算法，这是进行高效频繁项集挖掘的关键方法。 4. **离群点**： 本周将探讨离群点检测的重要性及其在识别异常数据点中的作用。 5. **案例研究**： 最后一周将集中于一个综合案例研究，您将应用关联规则挖掘和离群点检测技术来解决一个真实世界的问题。 该课程为期五周，适合希望深入了解数据分析与模式识别的学生。
Name:Frequent Itemsets
Description:This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.
Name:Association Rule Mining
Description:This week we will briefly discuss association rule mining, such as closed and maxed patterns.
Name:Apriori and FP Growth Algorithm
Description:This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.
Name:Outliers
Description:Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.
Name:Case Study
Description:The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.
The "Association Rules and Outliers Analysis" course introduces students to fundamental concepts of unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining. Additionally, students will explore outlier detection methods, with a deep understanding of contextual outliers. Through interactive tutorials and practical ca
