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

   

课程主页: https://www.coursera.org/course/clusteranalysis

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课程详情

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.

课程大纲

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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课程简介

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.

课程标签

数据挖掘 聚类分析

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