Data Mining Methods

Data Mining Methods

This course is part of Data Mining Foundations and Practice Specialization

Instructor: Qin (Christine) Lv

What you'll learn

Identify the core functionalities of data modeling in the data mining pipeline
Apply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work.
Evaluate data modeling techniques, determine which is most suitable for a particular task, and identify potential improvements.

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Taught in English

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This course is part of the Data Mining Foundations and Practice Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE

This week starts with an overview of this course, Data Mining Methods, then focuses on frequent pattern analysis, including the Apriori algorithm and FP-growth algorithm for frequent itemset mining, as well as association rules and correlation analysis.

What's included

15 videos3 readings1 programming assignment1 discussion prompt

15 videosTotal 152 minutes

Meet Your Instructor!4 minutesPreview module
Preparing for this Specialization9 minutes
Data Mining: Technique View11 minutes
Frequent Pattern Analysis, Apriori Algorithm 12 minutes
Apriori Algorithm Example, Details9 minutes
Example: Apriori Frequent Pattern Analysis8 minutes
Apriori Algorithm Challenges and Improvements13 minutes
FP-growth Algorithm, Example16 minutes
Association Rule, Example8 minutes
Correlation, Example13 minutes
Other Correlation Measures10 minutes
Example: FP-growth Frequent Pattern Analysis9 minutes
Example: Monotonic and Anti-monotonic Constraints9 minutes
Example: Lift Correlation7 minutes
Example: X^2 Correlation7 minutes

3 readingsTotal 30 minutes

Earn Academic Credit for Your Work! 10 minutes
Course Support10 minutes
About This Course10 minutes

1 programming assignmentTotal 240 minutes

Frequent Pattern Analysis240 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself!10 minutes

This week introduces supervised learning, classification, prediction, and covers several core classification methods including decision tree induction, Bayesian classification, support vector machines, neural networks, and ensemble methods. It also discusses classification model evaluation and comparison.

What's included

9 videos1 programming assignment

9 videosTotal 125 minutes

Introduction to Classification 15 minutesPreview module
Decision Tree Induction, Example20 minutes
Bayesian Classification, Example17 minutes
Example: Decision Tree Induction Classification 9 minutes
Example: Bayesian Classification8 minutes
Support Vector Machines9 minutes
Neural Network 12 minutes
Ensemble, Model Evaluation20 minutes
Model Selection13 minutes

1 programming assignmentTotal 240 minutes

Classification 240 minutes

This week introduces you to unsupervised learning, clustering, and covers several core clustering methods including partitioning, hierarchical, grid-based, density-based, and probabilistic clustering. Advanced topics for high-dimensional clustering, bi-clustering, graph clustering, and constraint-based clustering are also discussed.

What's included

8 videos1 reading1 programming assignment

8 videosTotal 106 minutes

Introduction to Clustering11 minutesPreview module
Partitioning Methods16 minutes
Hierarchical and Grid Based Clustering16 minutes
Density-Based Clustering10 minutes
Probabilistic Clustering10 minutes
EM Clustering11 minutes
High Dimensional, Bi-Clustering, Graph Clustering16 minutes
Constraint Based Clustering12 minutes

1 readingTotal 10 minutes

EM Clustering: Further Explanation 10 minutes

1 programming assignmentTotal 240 minutes

Clustering240 minutes

This week discusses three different types of outliers (global, contextual, and collective) and how different methods may be used to identify and analyze such outliers. It also covers some advanced methods for mining complex data, as well as the research frontiers of the data mining field.