Cluster Analysis in Data Mining

开始时间: 04/27/2015 持续时间: 4 weeks

所在平台: Coursera

课程类别: 计算机科学

大学或机构: University of Illinois at Urbana-Champaign( 伊利诺伊大学厄巴纳 - 香槟分校)

授课老师: Jiawei Han



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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 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
  • 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


Deep Learning Specialization on Coursera


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 taken as a pre-processing step for many data mining tasks.


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