Machine Learning Summer School (MLSS), Kioloa 2008

Contrast Data Mining: Methods and Applications

author: Rao Kotagiri, Department of Computer Science and Software Engineering, The University of Melbourne
published: March 12, 2008, recorded: March 2008, views: 759

Slides

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

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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.