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.
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 1 小時
Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
1 個視頻 （總計 7 分鐘）, 3 個閱讀材料, 1 個測驗
1 個視頻
3 個閱讀材料
Syllabus10分鐘
About the Discussion Forums10分鐘
Social Media10分鐘
1 個練習
Orientation Quiz10分鐘
完成時間為 4 小時
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 個視頻 （總計 49 分鐘）, 2 個閱讀材料, 3 個測驗
9 個視頻
1.2. Frequent Patterns and Association Rules5分鐘
1.3. Compressed Representation: Closed Patterns and Max-Patterns7分鐘
2.1. The Downward Closure Property of Frequent Patterns3分鐘
2.2. The Apriori Algorithm6分鐘
2.3. Extensions or Improvements of Apriori7分鐘
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3分鐘
2.5. FPGrowth: A Pattern Growth Approach8分鐘
2.6. Mining Closed Patterns3分鐘
2 個閱讀材料
Lesson 1 Overview10分鐘
Lesson 2 Overview10分鐘
2 個練習
Lesson 1 Quiz10分鐘
Lesson 2 Quiz8分鐘
週
2完成時間為 1 小時
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 個視頻 （總計 47 分鐘）, 2 個閱讀材料, 2 個測驗
9 個視頻
3.2. Interestingness Measures: Lift and χ25分鐘
3.3. Null Invariance Measures5分鐘
3.4. Comparison of Null-Invariant Measures7分鐘
4.1. Mining Multi-Level Associations4分鐘
4.2. Mining Multi-Dimensional Associations2分鐘
4.3. Mining Quantitative Associations4分鐘
4.4. Mining Negative Correlations6分鐘
4.5. Mining Compressed Patterns7分鐘
2 個閱讀材料
Lesson 3 Overview10分鐘
Lesson 4 Overview10分鐘
2 個練習
Lesson 3 Quiz10分鐘
Lesson 4 Quiz8分鐘
週
3完成時間為 2 小時
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 個視頻 （總計 56 分鐘）, 2 個閱讀材料, 2 個測驗
10 個視頻
5.2. GSP: Apriori-Based Sequential Pattern Mining3分鐘
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3分鐘
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4分鐘
5.5. CloSpan—Mining Closed Sequential Patterns3分鐘
6.1. Mining Spatial Associations4分鐘
6.2. Mining Spatial Colocation Patterns9分鐘
6.3. Mining and Aggregating Patterns over Multiple Trajectories9分鐘
6.4. Mining Semantics-Rich Movement Patterns3分鐘
6.5. Mining Periodic Movement Patterns7分鐘
2 個閱讀材料
Lesson 5 Overview10分鐘
Lesson 6 Overview10分鐘
2 個練習
Lesson 5 Quiz10分鐘
Lesson 6 Quiz8分鐘
週
4完成時間為 5 小時
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 個視頻 （總計 98 分鐘）, 2 個閱讀材料, 3 個測驗
9 個視頻
7.2. Previous Phrase Mining Methods10分鐘
7.3. ToPMine: Phrase Mining without Training Data12分鐘
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14分鐘
8.1. Frequent Pattern Mining in Data Streams19分鐘
8.2. Pattern Discovery for Software Bug Mining12分鐘
8.3. Pattern Discovery for Image Analysis6分鐘
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13分鐘
8.5. Advanced Topics on Pattern Discovery: Looking Forward4分鐘
2 個閱讀材料
Lesson 7 Overview10分鐘
Lesson 8 Overview10分鐘
2 個練習
Lesson 7 Quiz8分鐘
Lesson 8 Quiz8分鐘
4.3
41 個審閱熱門審閱
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.
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.
講師
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
領先獲取學位
此 課程 隸屬於 伊利诺伊大学香槟分校 提供的 100% 在線 Master in Computer Science。立即開始學習開放課程或專項課程，觀看 iMBA 教師的課程並完成自主學習作業。
完成每門課程後，您將獲得一個證書，您可以添加到 LinkedIn 和簡歷中。
如果申請並被錄取參加全部課程，您的課程將計入您的學位學習進程。
關於 伊利诺伊大学香槟分校
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.
關於 数据挖掘 專項課程
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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