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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-patterns
课程评论：没有评论
课程名称：数据挖掘中的模式发现 课程概览：本课程将帮助您了解数据挖掘的一般概念、基本方法和应用，然后深入探讨数据挖掘的一个子领域——模式发现。您将学习模式发现的深入概念、方法和应用。此外，课程还将介绍数据驱动的短语挖掘方法及一些模式发现的有趣应用。本课程为您提供实践和参与可扩展模式发现方法的机会，特别针对海量交易数据、模式评估度量的讨论，以及多种模式、序列模式和子图模式的挖掘方法的学习。 课程大纲： 第一部分：模块 1 - 模块 1 包含两个课时。课时 1 介绍模式发现的一般概念，包括频繁模式、闭合模式、最大模式和关联规则的基本概念。课时 2 探讨挖掘频繁模式的三种主要方法，包括频繁模式的向下封闭性（或 Apriori）特性，Apriori 算法、垂直数据格式探测方法，以及模式增长方法。我们还将讨论如何直接挖掘闭合模式的集合。 第二部分：模块 2 - 模块 2 包含两个课时：课时 3 和课时 4。课时 3 讨论模式评估，并学习在模式分析中应使用的有趣度量方法。我们将展示支持-置信框架在模式评估中的不足，甚至流行的提升度和卡方度量在某些情况下也可能不佳。我们引入无效性概念，并提出一种新的无效性不变度量用于模式评估。课时 4 研究挖掘多样化模式谱的问题，学习多层次关联、多维关联、定量关联、负相关、压缩模式和考虑冗余的模式的概念及其挖掘方法。 第三部分：模块 3 - 模块 3 包含两个课时：课时 5 和课时 6。课时 5 讨论序列模式的挖掘，我们将学习几种流行和高效的序列模式挖掘方法，包括基于 Apriori 的序列模式挖掘方法 GSP、基于垂直数据格式的序列模式方法 SPADE 和基于模式增长的序列模式挖掘方法 PrefixSpan。我们还将学习如何直接挖掘闭合序列模式。课时 6 研究时空和轨迹模式的挖掘作为一种模式挖掘应用，介绍几种流行的模式及其挖掘方法，包括挖掘空间关联、空间共现模式、在多条轨迹上挖掘和聚合模式、挖掘语义丰富的运动模式和周期性运动模式。 第四部分：模块 4 - 模块 4 包含两个课时：课时 7 和课时 8。课时 7 研究从文本数据中挖掘优质短语，作为第二种模式挖掘应用，主要介绍两种新型短语挖掘方法：ToPMine 和 SegPhrase，并展示频繁模式挖掘在海量文本数据中挖掘优质短语的重要角色。课时 8 将学习模式发现的几个高级主题，包括在数据流中挖掘频繁模式、软件缺陷挖掘的模式发现、图像分析的模式发现，以及模式发现与社会的关系：隐私保护的模式挖掘。最后，我们将展望模式挖掘研究与应用的未来。
Part: 1
Title:Module 1
Description:Module 1 consists of two lessons. Lesson 1 covers the general concepts of pattern discovery. This includes the basic concepts of frequent patterns, closed patterns, max-patterns, and association rules. Lesson 2 covers three major approaches for mining frequent patterns. We will learn the downward closure (or Apriori) property of frequent patterns and three major categories of methods for mining frequent patterns: the Apriori algorithm, the method that explores vertical data format, and the pattern-growth approach. We will also discuss how to directly mine the set of closed patterns.
Part: 2
Title:Module 2
Description:Module 2 covers two lessons: Lessons 3 and 4. In Lesson 3, we discuss pattern evaluation and learn what kind of interesting measures should be used in pattern analysis. We show that the support-confidence framework is inadequate for pattern evaluation, and even the popularly used lift and chi-square measures may not be good under certain situations. We introduce the concept of null-invariance and introduce a new null-invariant measure for pattern evaluation. In Lesson 4, we examine the issues on mining a diverse spectrum of patterns. We learn the concepts of and mining methods for multiple-level associations, multi-dimensional associations, quantitative associations, negative correlations, compressed patterns, and redundancy-aware patterns.
Part: 3
Title:Module 3
Description:Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications. We will introduce a few popular kinds of patterns and their mining methods, including mining spatial associations, mining spatial colocation patterns, mining and aggregating patterns over multiple trajectories, mining semantics-rich movement patterns, and mining periodic movement patterns.
Part: 4
Title:Week 4
Description:Module 4 consists of two lessons: Lessons 7 and 8. In Lesson 7, we study mining quality phrases from text data as the second kind of pattern mining application. We will mainly introduce two newer methods for phrase mining: ToPMine and SegPhrase, and show frequent pattern mining may be an important role for mining quality phrases in massive text data. In Lesson 8, we will learn several advanced topics on pattern discovery, including mining frequent patterns in data streams, pattern discovery for software bug mining, pattern discovery for image analysis, and pattern discovery and society: privacy-preserving pattern mining. Finally, we look forward to the future of pattern mining research and application exploration.
Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
