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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-mining-methods
课程评论：没有评论
课程名称：数据挖掘方法 概述：本课程涵盖数据挖掘中的核心技术，包括频繁模式分析、分类、聚类、异常值分析，以及复杂数据挖掘与数据挖掘领域的研究前沿。数据挖掘方法课程可以作为科罗拉多大学博尔德分校数据科学硕士学位（MS-DS）的一部分，通过Coursera平台获得学术学分。MS-DS是一个跨学科的学位，汇集了来自应用数学、计算机科学、信息科学等多个部门的教师。由于基于表现的招生标准，没有申请流程，MS-DS非常适合拥有广泛本科教育背景和/或计算机科学、信息科学、数学和统计学专业经验的个人。更多关于MS-DS项目的信息请访问 https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 1. **频繁模式分析**：本周介绍课程的概述，重点讲解频繁模式分析，包括频繁项集挖掘的Apriori算法和FP-growth算法，以及关联规则和相关性分析。 2. **分类**：本周介绍监督学习、分类和预测，涵盖多个核心分类方法，包括决策树诱导、贝叶斯分类、支持向量机、神经网络和集成方法。同时讨论分类模型的评估和比较。 3. **聚类**：本周介绍无监督学习和聚类，覆盖多个核心聚类方法，包括划分聚类、层次聚类、网格基础聚类、基于密度的聚类和基于概率的聚类。还讨论了高维聚类、双聚类、图聚类和约束聚类等高级主题。 4. **异常值分析**：本周讨论三种不同类型的异常值（全球异常、上下文异常和集体异常），以及如何使用不同的方法来识别和分析这些异常值。同时涵盖了一些用于挖掘复杂数据的高级方法，以及数据挖掘领域的研究前沿。
Name:Frequent Pattern Analysis
Description:This week starts with an overview of this course, Data Mining Methods, then focuses on frequent pattern analysis, including the Apriori algorithm and FP-growth algorithm for frequent itemset mining, as well as association rules and correlation analysis.
Name:Classification
Description:This week introduces supervised learning, classification, prediction, and covers several core classification methods including decision tree induction, Bayesian classification, support vector machines, neural networks, and ensemble methods. It also discusses classification model evaluation and comparison.
Name:Clustering
Description:This week introduces you to unsupervised learning, clustering, and covers several core clustering methods including partitioning, hierarchical, grid-based, density-based, and probabilistic clustering. Advanced topics for high-dimensional clustering, bi-clustering, graph clustering, and constraint-based clustering are also discussed.
Name:Outlier Analysis
Description:This week discusses three different types of outliers (global, contextual, and collective) and how different methods may be used to identify and analyze such outliers. It also covers some advanced methods for mining complex data, as well as the research frontiers of the data mining field.
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. Data Mining Methods can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
