University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

This course is part of Data Mining Specialization

Taught in English

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Jiawei Han

Instructor: Jiawei Han

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Gain insight into a topic and learn the fundamentals

4.3

(316 reviews)

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89%

17 hours (approximately)
Flexible schedule
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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.3

(316 reviews)

|

89%

17 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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This course is part of the Data Mining Specialization
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There are 5 modules in this course

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

What's included

1 video3 readings1 quiz1 discussion prompt1 plugin

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.

What's included

9 videos2 readings2 quizzes1 programming assignment

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.

What's included

9 videos2 readings2 quizzes

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.

What's included

10 videos2 readings2 quizzes

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.

What's included

9 videos2 readings2 quizzes1 programming assignment1 plugin

Instructor

Instructor ratings
4.3 (12 ratings)
Jiawei Han
University of Illinois at Urbana-Champaign
4 Courses65,508 learners

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Recommended if you're interested in Data Analysis

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