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[Coursera] Cluster Analysis in Data Mining @Illinois 课程总结

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rarezhang 发表于 2015-5-6 13:44:37 | 显示全部楼层 |阅读模式


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本帖最后由 rarezhang 于 2015-5-5 22:46 编辑

这们课由著名的Jiawei Han老师主讲。从小就看Jiawei 老师的书,现在能跟着他老人家上课还真是感慨啊~
Jiawei 老师的讲课风格就是PPT从头念到尾,好在PPT质量不错,和书的风格非常相似。

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Course Description
In this course you will learn concepts and methodologies for cluster analysis, which is also known as clustering, data segmentation, or unsupervised learning.  Cluster analysis is an important subfield of data mining.  We will introduce 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, probabilistic models and EM algorithm. We will also discuss methods for clustering validation, clustering high-dimensional data, constraint-based clustering, clustering graphs and networked data, and cluster analysis in heterogeneous networks.  Finally, we will discuss some advanced topics of cluster analysis, including clustering stream data and outlier detection by cluster analysis, and examine some examples of cluster analysis in applications. For more detailed course schedule and contents, please see Course Outline.">

Course Goals and Objectives
We assume learners have basic knowledge of computer science and statistics.  This course will give a general introduction to cluster analysis and present a set of basic and popular methodologies and algorithms for cluster analysis. Through participation in this course, we expect learners will master the concepts, algorithms, and techniques of popular clustering methods and will be able to apply such methods to some practical applications.

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optional programming assignment:






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