18-12-2012, 06:30 PM
Data Mining-Association Rules and Clustering
Data Mining-Association Rules.ppt (Size: 1.4 MB / Downloads: 139)
Predictive modeling
Similar to human learning experience
Use observations !
Form a model of important characteristics of some phenomenon
A “black box” that makes predictions about the future based on information from the past and present
Application: customer retention management, credit approval, direct marketing.
Definitions
Maps data item into one of several clusters, where clusters are natural groupings of data items based on similarity metrics or probability density models.
Multi-scale representation of data refers to visualization of the data at different ‘scales’, where the term scale may signify either unit, frequency, radius, window size or kernel parameters.
The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data
Drawing circles and shapes around dots representing objects!
Data Types in Cluster Analysis
Nominal Variables
A generalization of the binary variable in that it can take on more than two states.
For example, a color be white, green, blue, red.
How is dissimilarity computed?
Matching approach d(i,j)=(p-m)/p
M is the number of similar attributes between I and j
P is the number of total attributes between I and j