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.
Ce cours fait partie de la Spécialisation Exploration de données
La découverte de répétitions dans l'exploration de données
À propos de ce cours
9,120
9,120Compétences que vous acquerrez
StreamsSequential Pattern MiningData Mining AlgorithmsData Mining
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
11 heure pour terminer
Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
1 vidéo (Total 7 min), 3 lectures, 1 quiz
1 vidéo
Course Introduction7 min
3 lectures
Syllabus10 min
About the Discussion Forums10 min
Social Media10 min
1 exercice pour s'entraîner
Orientation Quiz10 min
4 heures pour terminer
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 vidéos (Total 49 min), 2 lectures, 3 quiz
9 vidéos
1.2. Frequent Patterns and Association Rules5 min
1.3. Compressed Representation: Closed Patterns and Max-Patterns7 min
2.1. The Downward Closure Property of Frequent Patterns3 min
2.2. The Apriori Algorithm6 min
2.3. Extensions or Improvements of Apriori7 min
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3 min
2.5. FPGrowth: A Pattern Growth Approach8 min
2.6. Mining Closed Patterns3 min
2 lectures
Lesson 1 Overview10 min
Lesson 2 Overview10 min
2 exercices pour s'entraîner
Lesson 1 Quiz10 min
Lesson 2 Quiz8 min
Semaine
21 heure pour terminer
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 vidéos (Total 47 min), 2 lectures, 2 quiz
9 vidéos
3.2. Interestingness Measures: Lift and χ25 min
3.3. Null Invariance Measures5 min
3.4. Comparison of Null-Invariant Measures7 min
4.1. Mining Multi-Level Associations4 min
4.2. Mining Multi-Dimensional Associations2 min
4.3. Mining Quantitative Associations4 min
4.4. Mining Negative Correlations6 min
4.5. Mining Compressed Patterns7 min
2 lectures
Lesson 3 Overview10 min
Lesson 4 Overview10 min
2 exercices pour s'entraîner
Lesson 3 Quiz10 min
Lesson 4 Quiz8 min
Semaine
32 heures pour terminer
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 vidéos (Total 56 min), 2 lectures, 2 quiz
10 vidéos
5.2. GSP: Apriori-Based Sequential Pattern Mining3 min
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3 min
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4 min
5.5. CloSpan—Mining Closed Sequential Patterns3 min
6.1. Mining Spatial Associations4 min
6.2. Mining Spatial Colocation Patterns9 min
6.3. Mining and Aggregating Patterns over Multiple Trajectories9 min
6.4. Mining Semantics-Rich Movement Patterns3 min
6.5. Mining Periodic Movement Patterns7 min
2 lectures
Lesson 5 Overview10 min
Lesson 6 Overview10 min
2 exercices pour s'entraîner
Lesson 5 Quiz10 min
Lesson 6 Quiz8 min
Semaine
45 heures pour terminer
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 vidéos (Total 98 min), 2 lectures, 3 quiz
9 vidéos
7.2. Previous Phrase Mining Methods10 min
7.3. ToPMine: Phrase Mining without Training Data12 min
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14 min
8.1. Frequent Pattern Mining in Data Streams19 min
8.2. Pattern Discovery for Software Bug Mining12 min
8.3. Pattern Discovery for Image Analysis6 min
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13 min
8.5. Advanced Topics on Pattern Discovery: Looking Forward4 min
2 lectures
Lesson 7 Overview10 min
Lesson 8 Overview10 min
2 exercices pour s'entraîner
Lesson 7 Quiz8 min
Lesson 8 Quiz8 min
4.3
41 avisMeilleurs avis
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.
Enseignant
Jiawei Han
Abel Bliss ProfessorDepartment of Computer Science
Commencez à travailler pour obtenir votre master
Ce cours fait partie du diplôme intégralement en ligne Master in Computer Science de Université de l'Illinois à Urbana-Champaign.
Si vous êtes admis au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
À propos de Université de l'Illinois à 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.
À propos de la Spécialisation Exploration de données
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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