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Jiawei Han
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.
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What's inside
Syllabus
Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
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Syllabus
Course Orientation
The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops foundational knowledge in data mining including general concepts, pattern discovery, phrase mining, and various applications
Focuses on one subfield pattern discovery with in-depth concepts, methods, and various applications
Taught by Jiawei Han, recognized as one of the leading researchers in data mining
Provides opportunities to practice methods on massive transactional data found in industry
Covers diverse patterns and evaluation measures, which enhances learners' ability to analyze patterns
Examines advanced topics in pattern discovery, including data stream mining and privacy-preserving techniques
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Reviews summary
In-depth pattern discovery in data mining
According to learners, this course offers an exceptionally thorough and in-depth exploration of pattern discovery in data mining. Students highlight the comprehensive coverage of fundamental concepts and advanced algorithms, including Apriori, SPADE, and PrefixSpan. Many appreciate the modules on real-world applications like text mining and spatiotemporal data analysis. While praised for its theoretical depth and valuable insights, some reviewers find the pace can be challenging, especially for those without a strong prior foundation in mathematics or data mining basics. The assignments and quizzes are often described as demanding but rewarding, effectively reinforcing the learned material. Overall, it is seen as a strong course for building a solid understanding.
Assignments reinforce learning but are tough.
"The assignments were difficult, but they really made me think and apply what I learned."
"Completing the homework took significant effort, but it solidified my understanding."
"While challenging, I found the assignments to be fair and representative of the material."
"Prepare to spend a good amount of time on the problem sets."
Explore real-world pattern discovery applications.
"The modules on spatiotemporal and text mining applications were particularly interesting and relevant."
"It was great to see how pattern discovery applies to different types of data, like phrases in text using ToPMine."
"Learning about mining patterns in trajectories and spatial data opened up new perspectives for me."
"The applied sections really helped connect the theory to practical use cases."
Covers key pattern mining algorithms.
"Learning about the different approaches like Apriori, vertical format methods, and pattern-growth was invaluable."
"The detailed explanation of algorithms like SPADE and PrefixSpan was a major strength of the course."
"They didn't just mention the algorithms; they explained how they work and their trade-offs."
"I now understand the mechanics behind many popular mining techniques."
Deep dive into pattern discovery fundamentals.
"This course provided a very thorough theoretical foundation in data mining, specifically pattern discovery methods."
"I really appreciated the deep dive into concepts like frequent patterns, closed patterns, and association rules."
"The course covers the material comprehensively, going beyond just surface level explanations."
"I feel like I have a much better grasp of the core concepts and algorithms after taking this."
Course moves quickly, can be demanding.
"The pace of the course can be quite fast, especially in the later modules."
"Sometimes I felt rushed trying to keep up with the new concepts introduced each week."
"Might be challenging if you are completely new to data mining or lack a strong math background."
"Requires dedication to keep up with the lectures and assignments."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Pattern Discovery in Data Mining with these
activities:
Connect with Experienced Data Miners
Show steps
Accelerate your learning journey by seeking guidance from experienced professionals in the field of pattern discovery.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through online platforms, industry events, or personal connections.
-
Reach out to mentors, briefly introduce yourself, and express your interest in their mentorship.
-
Attend meetings or schedule calls to discuss your progress, seek advice, and gain valuable insights.
Organize Course Materials
Show steps
Enhance your learning experience by organizing and reviewing course materials, improving retention and facilitating easy access to information.
Browse courses on
Organization
Show steps
-
Set up a dedicated folder or notebook for course materials.
-
Regularly file lecture notes, assignments, and other course-related documents.
-
Categorize and label materials for efficient retrieval.
-
Review and summarize key concepts to reinforce understanding.
Review Frequent Patterns
Show steps
Become more comfortable with the foundational concepts of pattern discovery before you start Module 1 of the course.
Show steps
-
Review the Definition and Examples of Frequent Patterns
-
Go Over Properties of Frequent Patterns
-
Explore Applications of Frequent Patterns
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a Glossary of Data Mining Terms
Show steps
Expand your vocabulary and enhance your understanding of key terms used in the field of data mining.
Browse courses on
Data Mining
Show steps
-
Collect definitions of important data mining terms from various sources.
-
Create a comprehensive glossary with clear and concise definitions.
-
Organize the glossary alphabetically or by category for easy reference.
Complete Problems on Frequent Pattern Mining Algorithms
Show steps
Solidify your knowledge of frequent pattern mining algorithms by solving example problems.
Show steps
-
Select a set of example problems focusing on Apriori, Pattern Growth, and FP-growth.
-
Go through algorithms, step-by-step, mining frequent patterns for each.
-
Review and analyze results to validate understanding.
Explore Applications of Pattern Discovery
Show steps
Expand your knowledge by familiarizing yourself with the diverse ways pattern discovery is utilized across industries.
Browse courses on
Pattern Discovery
Show steps
-
Identify different industries that leverage pattern discovery for decision-making.
-
Research and explore real-world examples of how pattern discovery is applied in these industries.
-
Analyze the impact and significance of pattern discovery in improving outcomes.
Volunteer as a Mentor for Beginners
Show steps
Deepen your understanding of pattern discovery concepts and make a positive impact by mentoring students new to the field.
Browse courses on
Mentoring
Show steps
-
Join online forums or discussion groups related to pattern discovery.
-
Identify beginners seeking guidance and offer your support.
-
Provide clear explanations, answer questions, and share your insights.
-
Encourage students to engage in active learning and foster a positive learning environment.
Develop a Visualization for a Data Mining Case Study
Show steps
Demonstrate your understanding of data mining techniques by creating a visual representation of insights gained from a case study.
Browse courses on
Data Visualization
Show steps
-
Select a data mining case study that aligns with your interests.
-
Extract key patterns and insights from the case study.
-
Choose an appropriate visualization method to effectively convey the insights.
-
Create a visually appealing and informative visualization.
-
Write a brief report summarizing your findings and the significance of the visualization.
Connect with Experienced Data Miners
Show steps
Accelerate your learning journey by seeking guidance from experienced professionals in the field of pattern discovery.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms, industry events, or personal connections.
- Reach out to mentors, briefly introduce yourself, and express your interest in their mentorship.
- Attend meetings or schedule calls to discuss your progress, seek advice, and gain valuable insights.
Organize Course Materials
Show steps
Enhance your learning experience by organizing and reviewing course materials, improving retention and facilitating easy access to information.
Browse courses on
Organization
Show steps
Show steps
Show steps
- Set up a dedicated folder or notebook for course materials.
- Regularly file lecture notes, assignments, and other course-related documents.
- Categorize and label materials for efficient retrieval.
- Review and summarize key concepts to reinforce understanding.
Review Frequent Patterns
Show steps
Become more comfortable with the foundational concepts of pattern discovery before you start Module 1 of the course.
Show steps
Show steps
Show steps
- Review the Definition and Examples of Frequent Patterns
- Go Over Properties of Frequent Patterns
- Explore Applications of Frequent Patterns
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a Glossary of Data Mining Terms
Show steps
Expand your vocabulary and enhance your understanding of key terms used in the field of data mining.
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Collect definitions of important data mining terms from various sources.
- Create a comprehensive glossary with clear and concise definitions.
- Organize the glossary alphabetically or by category for easy reference.
Complete Problems on Frequent Pattern Mining Algorithms
Show steps
Solidify your knowledge of frequent pattern mining algorithms by solving example problems.
Show steps
Show steps
Show steps
- Select a set of example problems focusing on Apriori, Pattern Growth, and FP-growth.
- Go through algorithms, step-by-step, mining frequent patterns for each.
- Review and analyze results to validate understanding.
Explore Applications of Pattern Discovery
Show steps
Expand your knowledge by familiarizing yourself with the diverse ways pattern discovery is utilized across industries.
Browse courses on
Pattern Discovery
Show steps
Show steps
Show steps
- Identify different industries that leverage pattern discovery for decision-making.
- Research and explore real-world examples of how pattern discovery is applied in these industries.
- Analyze the impact and significance of pattern discovery in improving outcomes.
Volunteer as a Mentor for Beginners
Show steps
Deepen your understanding of pattern discovery concepts and make a positive impact by mentoring students new to the field.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Join online forums or discussion groups related to pattern discovery.
- Identify beginners seeking guidance and offer your support.
- Provide clear explanations, answer questions, and share your insights.
- Encourage students to engage in active learning and foster a positive learning environment.
Develop a Visualization for a Data Mining Case Study
Show steps
Demonstrate your understanding of data mining techniques by creating a visual representation of insights gained from a case study.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Select a data mining case study that aligns with your interests.
- Extract key patterns and insights from the case study.
- Choose an appropriate visualization method to effectively convey the insights.
- Create a visually appealing and informative visualization.
- Write a brief report summarizing your findings and the significance of the visualization.
Career center
Learners who complete Pattern Discovery in Data Mining will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers use their knowledge of programming and data to design and develop data pipelines. These pipelines automate the processes of collecting, cleaning, and organizing data for analysis. Often, Data Engineers are responsible for building and maintaining data warehouses and data lakes in support of an organization's business intelligence and analytics teams. The course introduces learners to the fundamentals of data mining, including approaches like the Apriori algorithm, vertical data format exploration, and the pattern-growth approach. This foundation prepares learners to build a career as a Data Engineer where they may work with data mining tools and techniques on a daily basis.
Data Analyst
Data Analyst
Data Analysts provide organizations with actionable insights by analyzing data. They combine their knowledge of statistics, data mining, and programming to identify trends, extract meaningful patterns, and help organizations make data-driven decisions. Learners will be exposed to evaluation methods for patterns, such as lift and chi-square, and learn about better alternatives, such as null invariance. This skill directly supports the work of a Data Analyst by providing them with additional methods for extracting insights from data and communicating them to stakeholders.
Data Scientist
Data Scientist
Data Scientists combine their knowledge of programming, statistics, and data mining to build machine learning models. These models analyze massive datasets and perform tasks such as prediction, classification, and clustering. Learners will study methods for mining sequential patterns, such as GSP, SPADE, and PrefixSpan. This skill supports the work of a Data Scientist by providing them with the techniques needed to identify sequential patterns in their data, potentially improving the accuracy and performance of their models.
Research Scientist
Research Scientist
Research Scientists conduct scientific research with the goal of advancing knowledge and developing new technologies. They may specialize in a particular field, such as data mining, and work in academic institutions, government labs, or private companies. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining diverse kinds of patterns, and mining sequential patterns. It provides a solid foundation for a career as a Research Scientist in the field of data mining.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning models. They work on projects such as image recognition, natural language processing, and speech recognition. Learners who take this course will be introduced to pattern mining, which is a fundamental technique used in machine learning for tasks such as feature extraction and model selection. Additionally, this course provides insights into evaluating and interpreting patterns, which is crucial for the success of machine learning models.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They work with stakeholders to understand their needs and develop data-driven solutions. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Business Analysts who work with large datasets and need to be able to extract insights from data in order to make informed recommendations.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on projects such as operating systems, web applications, and mobile apps. Learners who take this course will gain a foundation in pattern discovery, which is a fundamental technique used in software engineering for tasks such as code optimization and bug detection. Additionally, this course provides insights into evaluating and interpreting patterns, which is crucial for the success of software systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in the financial industry, helping to make investment decisions. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining sequential patterns, and mining spatiotemporal and trajectory patterns. It provides a solid foundation for a career as a Quantitative Analyst, who needs to be able to identify and interpret patterns in financial data in order to make informed investment recommendations.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. Learners who take this course will gain a foundation in pattern discovery, which is a fundamental technique used in product management for tasks such as market research and customer segmentation. Additionally, this course provides insights into evaluating and interpreting patterns, which is crucial for the success of new products.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of fields, such as healthcare, education, and government. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Statisticians who work with large datasets and need to be able to extract insights from data in order to make informed recommendations.
Financial Analyst
Financial Analyst
Financial Analysts provide organizations with financial advice. They analyze financial data, make recommendations, and help organizations make investment decisions. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining sequential patterns, and mining spatiotemporal and trajectory patterns. It provides a solid foundation for a career as a Financial Analyst, who needs to be able to identify and interpret patterns in financial data in order to make informed investment recommendations.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. They work in a variety of industries, such as manufacturing, healthcare, and transportation. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining sequential patterns, and mining spatiotemporal and trajectory patterns. It provides a solid foundation for a career as an Operations Research Analyst, who needs to be able to identify and interpret patterns in data in order to make informed recommendations.
Data Architect
Data Architect
Data Architects design and build data architectures for organizations. They work with stakeholders to understand their data needs and develop data solutions that meet those needs. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Data Architects who need to be able to extract insights from data in order to design and build effective data architectures.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They work with users to ensure that databases are running smoothly and that data is secure. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Database Administrators who need to be able to extract insights from data in order to optimize database performance and security.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help organizations make better decisions. They work with stakeholders to understand their data needs and develop data-driven solutions. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Business Intelligence Analysts who need to be able to extract insights from data in order to make informed recommendations.
For more career information including salaries, visit:
OpenCourser.com/course/axyjz7/pattern
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Pattern Discovery in Data Mining.
Save
Provides a comprehensive overview of data mining concepts and techniques, and good reference for the course. It covers a wide range of topics, including data preprocessing, clustering, classification, association rule mining, and sequential pattern mining.
Focuses specifically on frequent pattern mining, and good resource for students who want to learn more about this topic. It covers a variety of algorithms for mining frequent patterns, as well as applications of frequent pattern mining in different domains.
Provides a good overview of machine learning techniques that can be used for data mining, and good resource for students who want to learn more about the underlying theory behind data mining algorithms.
Provides a comprehensive overview of mining massive datasets, and good resource for students who want to learn more about these topics.
Provides a more theoretical perspective on pattern discovery in data mining, and good resource for students who want to learn more about the underlying theory behind pattern mining algorithms.
Save
Provides a more theoretical perspective on pattern recognition and machine learning, and good resource for students who want to learn more about the underlying theory behind data mining algorithms.
Provides a comprehensive overview of advanced pattern mining techniques, and good resource for students who want to learn more about these topics.
Provides a comprehensive overview of pattern mining for social networks, and good resource for students who want to learn more about these topics.
Save
Provides a practical guide to data mining using the R programming language, and good resource for students who want to learn how to use R for data mining.
Focuses on the applications of data mining in business intelligence, and good resource for students who want to learn how to use data mining to solve business problems.
Save
Provides a practical guide to data mining using the Weka machine learning software, and good resource for students who want to learn how to use Weka for data mining.
Provides a gentle introduction to data mining, and good resource for students who are new to the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/axyjz7/pattern
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Jiawei Han
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آذربایجان دیلی
This course belongs to a
collection
called
Data Mining . It's comprised of six courses:
- Data Visualization
- Text Mining and Analytics
- Cluster Analysis in Data Mining
- Pattern Discovery in Data Mining (viewing)
- Text Retrieval and Search Engines
- Data Mining Project
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