About this Course
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다음 전문 분야의 6개 강좌 중 4번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 15시간 필요


자막: 영어

귀하가 습득할 기술

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining

다음 전문 분야의 6개 강좌 중 4번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 15시간 필요


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 1시간 필요

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.

1 video (Total 7 min), 3 readings, 1 quiz
1개의 동영상
3개의 읽기 자료
About the Discussion Forums10m
Social Media10m
1개 연습문제
Orientation Quiz10m
완료하는 데 4시간 필요

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 videos (Total 49 min), 2 readings, 3 quizzes
9개의 동영상
2.1. The Downward Closure Property of Frequent Patterns3m
2.2. The Apriori Algorithm6m
2.3. Extensions or Improvements of Apriori7m
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3m
2.5. FPGrowth: A Pattern Growth Approach8m
2.6. Mining Closed Patterns3m
2개의 읽기 자료
Lesson 1 Overview10m
Lesson 2 Overview10m
2개 연습문제
Lesson 1 Quiz10m
Lesson 2 Quiz8m
완료하는 데 1시간 필요

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 videos (Total 47 min), 2 readings, 2 quizzes
9개의 동영상
3.4. Comparison of Null-Invariant Measures7m
4.1. Mining Multi-Level Associations4m
4.2. Mining Multi-Dimensional Associations2m
4.3. Mining Quantitative Associations4m
4.4. Mining Negative Correlations6m
4.5. Mining Compressed Patterns7m
2개의 읽기 자료
Lesson 3 Overview10m
Lesson 4 Overview10m
2개 연습문제
Lesson 3 Quiz10m
Lesson 4 Quiz8m
완료하는 데 2시간 필요

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 videos (Total 56 min), 2 readings, 2 quizzes
10개의 동영상
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4m
5.5. CloSpan—Mining Closed Sequential Patterns3m
6.1. Mining Spatial Associations4m
6.2. Mining Spatial Colocation Patterns9m
6.3. Mining and Aggregating Patterns over Multiple Trajectories9m
6.4. Mining Semantics-Rich Movement Patterns3m
6.5. Mining Periodic Movement Patterns7m
2개의 읽기 자료
Lesson 5 Overview10m
Lesson 6 Overview10m
2개 연습문제
Lesson 5 Quiz10m
Lesson 6 Quiz8m
완료하는 데 5시간 필요

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 videos (Total 98 min), 2 readings, 3 quizzes
9개의 동영상
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14m
8.1. Frequent Pattern Mining in Data Streams19m
8.2. Pattern Discovery for Software Bug Mining12m
8.3. Pattern Discovery for Image Analysis6m
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13m
8.5. Advanced Topics on Pattern Discovery: Looking Forward4m
2개의 읽기 자료
Lesson 7 Overview10m
Lesson 8 Overview10m
2개 연습문제
Lesson 7 Quiz8m
Lesson 8 Quiz8m
42개의 리뷰Chevron Right

Pattern Discovery in Data Mining의 최상위 리뷰

대학: DDSep 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.

대학: GLJan 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.



Jiawei Han

Abel Bliss Professor
Department of Computer Science

Start working towards your Master's degree

이 강좌은(는) 일리노이대학교 어버너-섐페인캠퍼스의 100% 온라인 Master in Computer Science 중 일부입니다. 전체 프로그램을 수료하면 귀하의 강좌가 학위 취득에 반영됩니다.

일리노이대학교 어버너-섐페인캠퍼스 정보

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. ...

데이터 마이닝 전문 분야 정보

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....
데이터 마이닝

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