Pattern Discovery in Data Mining

Location type
Logo Coursera
Provider rating: starstarstarstar_borderstar_border 6.3 Coursera has an average rating of 6.3 (out of 4 reviews)

Need more information? Get more details on the site of the provider.

Description

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by: …

Read the complete description

Frequently asked questions

There are no frequently asked questions yet. Send an Email to info@springest.com

Didn't find what you were looking for? See also: Data Mining, Computer Science, Hour of Code, SQL, and Visualization.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  University of Illinois at Urbana-Champaign
  • Taught by:  Jiawei Han, Abel Bliss Professor

    Department of Computer Science
Basic Info Course 4 of 6 in the Data Mining Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said 课程作业

每门课程都像是一本互动的教科书,具有预先录制的视频、测验和项目。

来自同学的帮助

与其他成千上万的学生相联系,对想法进行辩论,讨论课程材料,并寻求帮助来掌握概念。

证书

获得正式认证的作业,并与朋友、同事和雇主分享您的成功。

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.

Syllabus


WEEK 1


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


1 video, 3 readings, 1 practice quiz expand


  1. Video: Course Introduction
  2. 阅读: Syllabus
  3. 阅读: About the Discussion Forums
  4. 讨论提示: Getting to Know Your Classmates
  5. 阅读: Social Media
  6. 练习测验: 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 videos, 2 readings expand


  1. 阅读: Lesson 1 Overview
  2. Video: 1.1. What Is Pattern Discovery? Why Is It Important?
  3. Video: 1.2. Frequent Patterns and Association Rules
  4. Video: 1.3. Compressed Representation: Closed Patterns and Max-Patterns
  5. 阅读: Lesson 2 Overview
  6. Video: 2.1. The Downward Closure Property of Frequent Patterns
  7. Video: 2.2. The Apriori Algorithm
  8. Video: 2.3. Extensions or Improvements of Apriori
  9. Video: 2.4. Mining Frequent Patterns by Exploring Vertical Data Format
  10. Video: 2.5. FPGrowth: A Pattern Growth Approach
  11. Video: 2.6. Mining Closed Patterns

Graded: Lesson 1 Quiz
Graded: Lesson 2 Quiz
Graded: Frequent Itemset Mining Using Apriori

WEEK 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 videos, 2 readings expand


  1. 阅读: Lesson 3 Overview
  2. Video: 3.1. Limitation of the Support-Confidence Framework
  3. Video: 3.2. Interestingness Measures: Lift and χ2
  4. Video: 3.3. Null Invariance Measures
  5. Video: 3.4. Comparison of Null-Invariant Measures
  6. 阅读: Lesson 4 Overview
  7. Video: 4.1. Mining Multi-Level Associations
  8. Video: 4.2. Mining Multi-Dimensional Associations
  9. Video: 4.3. Mining Quantitative Associations
  10. Video: 4.4. Mining Negative Correlations
  11. Video: 4.5. Mining Compressed Patterns

Graded: Lesson 3 Quiz
Graded: Lesson 4 Quiz

WEEK 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 videos, 2 readings expand


  1. 阅读: Lesson 5 Overview
  2. Video: 5.1. Sequential Pattern and Sequential Pattern Mining
  3. Video: 5.2. GSP: Apriori-Based Sequential Pattern Mining
  4. Video: 5.3. SPADE—Sequential Pattern Mining in Vertical Data Format
  5. Video: 5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth
  6. Video: 5.5. CloSpan—Mining Closed Sequential Patterns
  7. 阅读: Lesson 6 Overview
  8. Video: 6.1. Mining Spatial Associations
  9. Video: 6.2. Mining Spatial Colocation Patterns
  10. Video: 6.3. Mining and Aggregating Patterns over Multiple Trajectories
  11. Video: 6.4. Mining Semantics-Rich Movement Patterns
  12. Video: 6.5. Mining Periodic Movement Patterns

Graded: Lesson 5 Quiz
Graded: Lesson 6 Quiz

WEEK 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 videos, 2 readings expand


  1. 阅读: Lesson 7 Overview
  2. Video: 7.1. From Frequent Pattern Mining to Phrase Mining
  3. Video: 7.2. Previous Phrase Mining Methods
  4. Video: 7.3. ToPMine: Phrase Mining without Training Data
  5. Video: 7.4. SegPhrase: Phrase Mining with Tiny Training Sets
  6. 未分级程序开发: Mining Contiguous Sequential Patterns in Text
  7. 阅读: Lesson 8 Overview
  8. Video: 8.1. Frequent Pattern Mining in Data Streams
  9. Video: 8.2. Pattern Discovery for Software Bug Mining
  10. Video: 8.3. Pattern Discovery for Image Analysis
  11. Video: 8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue
  12. Video: 8.5. Advanced Topics on Pattern Discovery: Looking Forward

Graded: Lesson 7 Quiz
Graded: Lesson 8 Quiz
There are no reviews yet.

Share your review

Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate $1.- to Stichting Edukans.

There are no frequently asked questions yet. Send an Email to info@springest.com