Course Summary
This course teaches students how to identify and analyze patterns within data sets. It covers a wide range of techniques including clustering, classification, and anomaly detection.Key Learning Points
- Learn to identify patterns within data sets
- Understand techniques such as clustering and classification
- Develop skills in data analysis and visualization
Related Topics for further study
Learning Outcomes
- Identify patterns within data sets
- Apply clustering and classification techniques
- Develop skills in data analysis and visualization
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with programming concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Introduction to Data Science
- Data Mining
Related Education Paths
Related Books
Description
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.
Outline
- Course Orientation
- Course Introduction
- Syllabus
- About the Discussion Forums
- Social Media
- Orientation Quiz
- Module 1
- 1.1. What Is Pattern Discovery? Why Is It Important?
- 1.2. Frequent Patterns and Association Rules
- 1.3. Compressed Representation: Closed Patterns and Max-Patterns
- 2.1. The Downward Closure Property of Frequent Patterns
- 2.2. The Apriori Algorithm
- 2.3. Extensions or Improvements of Apriori
- 2.4. Mining Frequent Patterns by Exploring Vertical Data Format
- 2.5. FPGrowth: A Pattern Growth Approach
- 2.6. Mining Closed Patterns
- Lesson 1 Overview
- Lesson 2 Overview
- Lesson 1 Quiz
- Lesson 2 Quiz
- Module 2
- 3.1. Limitation of the Support-Confidence Framework
- 3.2. Interestingness Measures: Lift and χ2
- 3.3. Null Invariance Measures
- 3.4. Comparison of Null-Invariant Measures
- 4.1. Mining Multi-Level Associations
- 4.2. Mining Multi-Dimensional Associations
- 4.3. Mining Quantitative Associations
- 4.4. Mining Negative Correlations
- 4.5. Mining Compressed Patterns
- Lesson 3 Overview
- Lesson 4 Overview
- Lesson 3 Quiz
- Lesson 4 Quiz
- Module 3
- 5.1. Sequential Pattern and Sequential Pattern Mining
- 5.2. GSP: Apriori-Based Sequential Pattern Mining
- 5.3. SPADE—Sequential Pattern Mining in Vertical Data Format
- 5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth
- 5.5. CloSpan—Mining Closed Sequential Patterns
- 6.1. Mining Spatial Associations
- 6.2. Mining Spatial Colocation Patterns
- 6.3. Mining and Aggregating Patterns over Multiple Trajectories
- 6.4. Mining Semantics-Rich Movement Patterns
- 6.5. Mining Periodic Movement Patterns
- Lesson 5 Overview
- Lesson 6 Overview
- Lesson 5 Quiz
- Lesson 6 Quiz
- Week 4
- 7.1. From Frequent Pattern Mining to Phrase Mining
- 7.2. Previous Phrase Mining Methods
- 7.3. ToPMine: Phrase Mining without Training Data
- 7.4. SegPhrase: Phrase Mining with Tiny Training Sets
- 8.1. Frequent Pattern Mining in Data Streams
- 8.2. Pattern Discovery for Software Bug Mining
- 8.3. Pattern Discovery for Image Analysis
- 8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue
- 8.5. Advanced Topics on Pattern Discovery: Looking Forward
- Lesson 7 Overview
- Lesson 8 Overview
- Lesson 7 Quiz
- Lesson 8 Quiz
Jiawei Han
4.3 Raiting
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