Об этом курсе: 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.
Автор: University of Illinois at Urbana-Champaign
Преподаватели: Jiawei Han, Abel Bliss Professor
Department of Computer Science
| Основные сведения | Курс 4 из 6 — Data Mining Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
1 видео, 3 материала для самостоятельного изучения, 1 тренировочный тест
- Reading: Syllabus
- Reading: About the Discussion Forums
- Discussion Prompt: Getting to Know Your Classmates
- Reading: Social Media
- Practice Quiz: Orientation Quiz
Module 1
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.
9 видео, 2 материала для самостоятельного изучения
- Reading: Lesson 1 Overview
- Видео: 1.2. Frequent Patterns and Association Rules
- Видео: 1.3. Compressed Representation: Closed Patterns and Max-Patterns
- Reading: Lesson 2 Overview
- Видео: 2.1. The Downward Closure Property of Frequent Patterns
- Видео: 2.2. The Apriori Algorithm
- Видео: 2.3. Extensions or Improvements of Apriori
- Видео: 2.4. Mining Frequent Patterns by Exploring Vertical Data Format
- Видео: 2.5. FPGrowth: A Pattern Growth Approach
- Видео: 2.6. Mining Closed Patterns
Оцениваемый: Lesson 1 Quiz
Оцениваемый: Lesson 2 Quiz
Оцениваемый: Frequent Itemset Mining Using Apriori
НЕДЕЛЯ 2
Module 2
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.
9 видео, 2 материала для самостоятельного изучения
- Reading: Lesson 3 Overview
- Видео: 3.2. Interestingness Measures: Lift and χ2
- Видео: 3.3. Null Invariance Measures
- Видео: 3.4. Comparison of Null-Invariant Measures
- Reading: Lesson 4 Overview
- Видео: 4.1. Mining Multi-Level Associations
- Видео: 4.2. Mining Multi-Dimensional Associations
- Видео: 4.3. Mining Quantitative Associations
- Видео: 4.4. Mining Negative Correlations
- Видео: 4.5. Mining Compressed Patterns
Оцениваемый: Lesson 3 Quiz
Оцениваемый: Lesson 4 Quiz
НЕДЕЛЯ 3
Module 3
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.
10 видео, 2 материала для самостоятельного изучения
- Reading: Lesson 5 Overview
- Видео: 5.2. GSP: Apriori-Based Sequential Pattern Mining
- Видео: 5.3. SPADE—Sequential Pattern Mining in Vertical Data Format
- Видео: 5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth
- Видео: 5.5. CloSpan—Mining Closed Sequential Patterns
- Reading: Lesson 6 Overview
- Видео: 6.1. Mining Spatial Associations
- Видео: 6.2. Mining Spatial Colocation Patterns
- Видео: 6.3. Mining and Aggregating Patterns over Multiple Trajectories
- Видео: 6.4. Mining Semantics-Rich Movement Patterns
- Видео: 6.5. Mining Periodic Movement Patterns
Оцениваемый: Lesson 5 Quiz
Оцениваемый: Lesson 6 Quiz
НЕДЕЛЯ 4
Week 4
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.
9 видео, 2 материала для самостоятельного изучения
- Reading: Lesson 7 Overview
- Видео: 7.2. Previous Phrase Mining Methods
- Видео: 7.3. ToPMine: Phrase Mining without Training Data
- Видео: 7.4. SegPhrase: Phrase Mining with Tiny Training Sets
- Ungraded Programming: Mining Contiguous Sequential Patterns in Text
- Reading: Lesson 8 Overview
- Видео: 8.1. Frequent Pattern Mining in Data Streams
- Видео: 8.2. Pattern Discovery for Software Bug Mining
- Видео: 8.3. Pattern Discovery for Image Analysis
- Видео: 8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue
- Видео: 8.5. Advanced Topics on Pattern Discovery: Looking Forward
Оцениваемый: Lesson 7 Quiz
Оцениваемый: Lesson 8 Quiz
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Авторы
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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Рейтинги и отзывы
Very usefull course. I learned too much about pattern discovering.
Gave a very good introduction of Pattern Discovery and different mechanisms/algorithms for pattern discovery. Talks about different pattern discovery approaches, pros & cons of each. Found it very helpful for my investment analysis project
Very well-organised course. I especially liked the assignments: programming assignments were helpful to apply the course (this makes you implement the methods you learn), so were multiple-choice questions that really make you think on the course content. The instructor was clear and provided good materials. I would recommend this course.
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IW
compact class which teaches you lots of knowledge without wasting any time, using frequent tests to renew your memory and test your comprehension.
The programming assignment is a little bit challenging though. I would like to post a guide if I figure out how to get 100/100.