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Cluster Analysis in Data Mining

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 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.

Course Fee:

Course Type:


Course Status:



1 - 4 hours / week

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