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.
Этот курс входит в специализацию ''Специализация Data Mining '
Pattern Discovery in Data Mining
от партнера
Об этом курсе
4.3
Оценки: 160
Приобретаемые навыки
StreamsSequential Pattern MiningData Mining AlgorithmsData Mining
Программа курса: что вы изучите
Неделя
1Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
1 видео ((всего 7 мин.)), 3 материалов для самостоятельного изучения, 1 тест
1 видео
3 материала для самостоятельного изучения
Syllabus10мин
About the Discussion Forums10мин
Social Media10мин
1 практическое упражнение
Orientation Quiz10мин
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 ((всего 49 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
9 видео
1.2. Frequent Patterns and Association Rules5мин
1.3. Compressed Representation: Closed Patterns and Max-Patterns7мин
2.1. The Downward Closure Property of Frequent Patterns3мин
2.2. The Apriori Algorithm6мин
2.3. Extensions or Improvements of Apriori7мин
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3мин
2.5. FPGrowth: A Pattern Growth Approach8мин
2.6. Mining Closed Patterns3мин
2 материала для самостоятельного изучения
Lesson 1 Overview10мин
Lesson 2 Overview10мин
2 практического упражнения
Lesson 1 Quiz10мин
Lesson 2 Quiz8мин
Неделя
2Module 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 ((всего 47 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
9 видео
3.2. Interestingness Measures: Lift and χ25мин
3.3. Null Invariance Measures5мин
3.4. Comparison of Null-Invariant Measures7мин
4.1. Mining Multi-Level Associations4мин
4.2. Mining Multi-Dimensional Associations2мин
4.3. Mining Quantitative Associations4мин
4.4. Mining Negative Correlations6мин
4.5. Mining Compressed Patterns7мин
2 материала для самостоятельного изучения
Lesson 3 Overview10мин
Lesson 4 Overview10мин
2 практического упражнения
Lesson 3 Quiz10мин
Lesson 4 Quiz8мин
Неделя
3Module 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 ((всего 56 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
10 видео
5.2. GSP: Apriori-Based Sequential Pattern Mining3мин
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3мин
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4мин
5.5. CloSpan—Mining Closed Sequential Patterns3мин
6.1. Mining Spatial Associations4мин
6.2. Mining Spatial Colocation Patterns9мин
6.3. Mining and Aggregating Patterns over Multiple Trajectories9мин
6.4. Mining Semantics-Rich Movement Patterns3мин
6.5. Mining Periodic Movement Patterns7мин
2 материала для самостоятельного изучения
Lesson 5 Overview10мин
Lesson 6 Overview10мин
2 практического упражнения
Lesson 5 Quiz10мин
Lesson 6 Quiz8мин
Неделя
4Week 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 ((всего 98 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
9 видео
7.2. Previous Phrase Mining Methods10мин
7.3. ToPMine: Phrase Mining without Training Data12мин
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14мин
8.1. Frequent Pattern Mining in Data Streams19мин
8.2. Pattern Discovery for Software Bug Mining12мин
8.3. Pattern Discovery for Image Analysis6мин
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13мин
8.5. Advanced Topics on Pattern Discovery: Looking Forward4мин
2 материала для самостоятельного изучения
Lesson 7 Overview10мин
Lesson 8 Overview10мин
2 практического упражнения
Lesson 7 Quiz8мин
Lesson 8 Quiz8мин
4.3
Рецензии: 33Лучшие рецензии
автор: GL•Jan 18th 2018
Excellent course. Now I have a big picture about pattern discovery and understand some popular algorithm. Also professor points out the direction for further study.
автор: DD•Sep 10th 2017
The first several chapters are very impressive. The last three lessons are a little difficult for first-learners. The illustration are clear and easy to understand.
Преподаватель
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
Сделайте шаг навстречу диплому магистра.
курс входит в онлайн-программу ''Master in Computer Science' от партнера University of Illinois at Urbana-Champaign.
Если вы переходите на полную программу, курсы засчитываются при получении диплома.
О 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.
О специализации ''Data Mining '
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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