隶属于 数据挖掘 专项课程 »
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.
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.
This course will be covering the following topics:
Basic concept and introduction
Probabilistic models and EM algorithm
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
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.
The course will have video lectures, accompanied by quizzes and peer graded assignments.
How does this course fit into the Data Mining Specialization?
This is the third course in the track.