Clustering - An overview
author:
Marina Meila,
Department of Statistics, University of Washington
Description
Clustering, or finding groups in data, is as old as machine learning itself, if not older. However, as more people use clustering in a variety of settings, the last few years we have brought unprecedented developments in this field. This tutorial will survey the most important clustering methods in use today from a unifying perspective. I will then present some of the current paradigms shifts in data clustering.
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| Slides | |
| 0:00 | Clustering - A tutorial overview |
| 0:06 | Clustering - A tutorial overview |
| 0:33 | Outline |
| 2:39 | Selecting K |
| 13:47 | The gap statistic |
| 17:29 | Practicalities |
| 20:30 | The KL statistic |
| 25:44 | Stability methods for choosing K |
| 34:11 | Clustering with outliers |
| 45:24 | To come next |
| 45:42 | Algorithms based on |
| 50:59 | Kernel density estimation |
| 54:54 | The Nugent-Stuetzle algorithm |
| 65:11 | Mean shift algorithms |
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