Cluster Analysis in Data Mining
Coursera
Course Summary
Learn how to take scattered data and organize it into groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks.
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Course Description
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, density-based methods such as DBSCAN/OPTICS, probabilistic models, and the EM algorithm. Learn clustering and methods for clustering high dimensional data, streaming data, graph data, and networked data. Explore concepts and methods for constraint-based clustering and semi-supervised clustering. Finally, see examples of cluster analysis in applications.
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Course Syllabus
This course will be covering the following topics:
- Basic concept and introduction
- Partitioning methods
- Hierarchical methods
- Density-based methods
- Probabilistic models and EM algorithm
- Spectral clustering
- Clustering high dimensional data
- Clustering streaming data
- Clustering graph data and network data
- Constraint-based clustering and semi-supervised clustering
- Application examples of cluster analysis
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Recommended Background
For this course you need basic computing proficiency including some programming experience in a typical programming language, such as C++, Java, or Python, and basic knowledge of database concepts, artificial intelligence, and statistics.
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Course Format
The course will have video lectures, accompanied by quizzes and peer graded assignments.
