Contrast Data Mining: Methods and Applications
author:
Rao Kotagiri,
University of Melbourne
Description
The ability to distinguish, differentiate and contrast between
different datasets is a key objective in data mining. Such an
ability can assist domain experts to understand their data, and can
help in building classification models. His presentation will
introduce the principal techniques for contrasting different types
of data, covering the main dataset varieties such as relational,
sequence, and graph forms of data, clusters, as well as data cubes.
It will also focus on some important real world application areas
that illustrate how mining contrasts is advantageous.
You might be experiencing some problems with Your Video player.
| Slides | |
| 0:00 | Contrast Data Mining: Methods and Applications |
| 2:13 | Contrast data mining -What is it ? |
| 2:41 | Contrast Data Mining -What is it ? Cont. |
| 4:20 | What can be contrasted ? |
| 6:06 | What can be contrasted ? Cont. |
| 8:13 | Alternative names for contrast data mining |
| 10:07 | Characteristics of contrast data mining |
| 12:04 | Contrast characteristics Cont. |
| 17:03 | How is contrast data mining used ? |
| 21:21 | Goals of this tutorial |
| 21:52 | By the end of this tutorial you will be able to … |
| 22:12 | Don‟t have time to cover .. |
| 25:04 | Outline of the tutorial |
| 25:40 | Basic notions and univariate case |
| 27:03 | Sample Feature-Class |
| 28:01 | Discriminative power |
| 29:57 | Example Discriminative Power Test -Wilcoxon Rank Sum |
| 31:23 | Rank Sum Calculation Example |
| 33:05 | Wilcoxon Rank Sum TestCont. |
| 35:34 | Discriminating with attribute values |
| 38:14 | Attribute/Feature Conversion |
| 38:21 | Discriminating with attribute values |
| 39:48 | Attribute/Feature Conversion |
| 41:01 | Discriminating Attribute Values in a Data Stream |
| 42:07 | Odds ratio and Risk ratio |
| 42:39 | Odds and risk ratio Cont. |
| 43:08 | Odds Ratio Example |
| 44:29 | Relative Risk Example |
| 46:31 | Pattern/Rule Based Contrasts |
| 49:26 | Overview |
Lecture rating
| People found this lecture: | ||
| Worth seeing | ||
| because it is: | ||
| Valuable and informative | ||
| Well presented | ||
| Easily understandable | ||
| Acceptably recorded | ||
| You need to login to cast your vote. | ||
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Related content
Visitors who watched this lecture also watched...
Watch videos: (click on thumbnail to launch)
SEE ALSO:
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !





