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所在平台: Udemy |
课程主页: https://www.udemy.com/course/cluster-analysis-motivation-theory-practical-application/
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**课程名称：** Cluster Analysis- Theory & workout using SAS and R **课程概述：** 本课程深入探讨了数据挖掘中最流行的技术之一——聚类分析，特别侧重于其在市场营销领域的应用。聚类分析的核心在于识别数据中“自然”的群体，使得同一群体内的成员尽可能相似，而不同群体间的成员尽可能相异。 **课程内容：** 课程内容丰富，包括： * **理论基础：** 详细讲解聚类分析的动机、学习价值、与其他 segmentation 方法（如 CHAID/CART）的区别；阐述统计基础和实际应用。 * **聚类类型：** 介绍不同类型的聚类分析。 * **高层视图：** 提供聚类分析的整体概览。 * **层次聚类：** 深入讲解凝聚型（Agglomerative）和分裂型（Divisive）技术，以及如何理解和解释树状图（Dendogram）和碎石图（Scree plot）来决定聚类数量。 * **SAS 实现：** 教授如何使用 SAS 命令执行层次聚类，何时以及为何需要标准化数据，并指导如何理解和解读 SAS 输出。 * **非层次聚类（K-Means）：** 解释 K-Means 方法的必要性、工作原理、迭代过程以及如何决定合并或分割簇。 * **SAS 实现（K-Means）：** 教授如何使用 SAS 命令执行 K-Means 聚类，并指导如何理解和解读输出。 * **R 语言实现：** 学习使用 R 语言进行层次聚类和非层次聚类。 * **数据挖掘场景：** 将聚类分析应用于实际数据挖掘场景。 * **实践练习：** 包含关于聚类分析的作业，以巩固所学知识。 **课程形式：** 包括带有语音解说的 PowerPoint 视频演示、PDF 文档、Excel 工作簿和 SAS 代码。 **课程时长：** 预计学习和内化课程概念需要约 10 小时。
About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes. Course duration- The course should take roughly 10 hours to understand and internalize the concepts. Course Structure (contents) The structure of the course is as follows. Part 01 - cluster analysis theory and workout using SAS --------------- Motivation - Where one applies cluster analysis. Why one should learn cluster analysis? How it is different from objective segmentation (CHAID / CART ) Statistical foundation and practical application: Understand Different type of cluster analysis Cluster Analysis - high level view Hierarchical clustering - Agglomerative or Divisive technique Dendogram - What it is? What does it show? Scree plot - How to decide about number of clusters How to use SAS command to run hierarchical clustering When and why does on need to standardize the data? How to understand and interpret the output Non-hierarchical clustering (K means clustering). Why do we need k means approach How does it work? How does it iterate? How does it decide about combining old clusters? How to use SAS command to run hierarchical clustering When and why does on need to standardize the data? How to understand and interpret the output Part 02 ----------- Learn R syntax for hierarchical and non hierarchical clustering Part 03 --------- Cluster analysis in data mining scenario Part 04 -------- Assignment on cluster analysis
