Summer Schools in Logic and Learning, Canberra 2009

Data Mining

author: Rao Kotagiri, Department of Computer Science and Software Engineering, The University of Melbourne
published: April 1, 2009, recorded: February 2009, views: 2440

Slides

Slides
0:00	Contrast pattern mining and its applications
7:41	Contrast data mining -What is it ? (1)
8:04	Contrast data mining -What is it ? (2)
9:27	What can be contrasted ? (1)
12:55	What can be contrasted ? (2)
13:57	Alternative names for contrast data mining
14:40	Characteristics of contrast data mining
20:08	Contrast characteristics
24:51	How is contrast data mining used ?
35:08	Goals of this tutorial
35:51	By the end of this tutorial you will be able to
37:16	Don’t have time to cover
42:26	Outline of the tutorial
42:58	Basic notions and univariate case
46:03	Sample Feature-Class
47:34	Discriminative power
48:01	Example Discriminative Power Test -Wilcoxon Rank Sum
48:51	Rank Sum Calculation Example
53:28	Wilcoxon Rank Sum Test
54:04	Discriminating with attribute values
56:19	Attribute/Feature Conversion
57:20	Discriminating Attribute Values in a Data Stream

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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. This presentation will introduce the principal techniques for contrasting datasets. It will also focus on some important real world application areas that illustrate how mining contrasts is advantageous.