Pattern Discovery in Data Mining

Go to Course: https://www.coursera.org/learn/data-patterns

Introduction

### Course Review: Pattern Discovery in Data Mining **Overview** If you are looking to deepen your understanding of data mining and specifically want to focus on the exciting realm of pattern discovery, the course "Pattern Discovery in Data Mining" on Coursera is an excellent choice. This course not only covers foundational concepts of data mining but also delves into specialized methodologies and applications that can grant you a robust skill set for tackling real-world data challenges. ### Course Structure and Content The course is well-organized into four comprehensive modules, each thoughtfully structured to facilitate a gradual learning process. #### Module 1: Introduction to Pattern Discovery The course kicks off with foundational concepts of pattern discovery, including frequent patterns, closed patterns, max-patterns, and association rules. The emphasis on different methodologies, such as the Apriori algorithm and pattern-growth approaches, equips learners with essential knowledge to initiate their journey in data mining. #### Module 2: Evaluation and Diverse Patterns Module 2 tackles the complexities involved in evaluating patterns. It challenges traditional methods like the support-confidence framework and introduces innovative evaluation measures grounded in null-invariance. This module is essential for those aspiring to undertake nuanced pattern analysis and use diverse mining techniques that can derive insights from multiple levels and associations. #### Module 3: Sequential and Spatiotemporal Patterns Moving beyond static patterns, Module 3 focuses on sequential patterns with advanced methods like GSP and PrefixSpan. The exploration extends to spatiotemporal and trajectory patterns, since they are increasingly relevant in a data-driven world where geographic and temporal contexts significantly influence data interpretation and applications. #### Module 4: Advanced Applications and Future Directions The final module introduces learners to practical applications like quality phrase mining using ToPMine and SegPhrase. It also discusses pattern discovery in emerging areas, such as software bug detection and privacy-preserving methods. This module not only solidifies the acquired knowledge but also opens discussions on future research opportunities, making it particularly valuable for learners keen on staying ahead in this rapidly evolving field. ### Learning Experience The course is structured with a combination of theoretical lessons and practical applications, ensuring that you can engage actively with the content. It encourages interaction with instructors and fellow students, enriching the learning experience through shared insights and collaborative problem-solving. The use of real-world examples in lessons fosters a deeper understanding of theoretical concepts, which is crucial for any practitioner in the field. Each module contains quizzes and assignments that reinforce the material, allowing you to apply what you’ve learned and solidify your understanding. ### Who Should Take This Course? This course is tailored for individuals with a background in data science, analytics, or related fields. Whether you are a student looking to deepen your knowledge base or a professional aiming to refine your skills, you will find valuable insights and tools within this course. It is also beneficial for those involved in software development, research, or any positions requiring data-driven decision-making. ### Final Thoughts and Recommendation The "Pattern Discovery in Data Mining" course on Coursera is a comprehensive exploration of one of the most exciting areas of data science. With its well-organized modules and rich content, you will gain practical skills and theoretical knowledge essential for tackling various data mining challenges. I highly recommend this course for anyone passionate about data mining and looking to leverage insights from complex data sets. By completing this course, you not only elevate your skill set but also position yourself favorably in the increasingly competitive landscape of data science. Enroll today and embark on your journey toward mastering pattern discovery!

Syllabus

Course Orientation

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

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.

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.

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.

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.