Download Features in Data Mining - The University of Iowa

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Transcript
Features in Data Mining
Andrew Kusiak
Intelligent Systems Laboratory
2139 Seamans Center
The University of Iowa
Iowa City, IA 52242 - 1527
[email protected]
http://www.icaen.uiowa.edu/~ankusiak
Tel. 319-335 5934
Data Objects
Example
Feature (Attribute)
F1 F2
F3
F4
Decision
7
3.4
Cold
Appropriate
Yes
Feature value
Types of Features (Attributes)
❏ Continuous
quantitative feature
❏ Discrete quantitative feature
❏ Ordinal qualitative feature
❏ Nominal qualitative feature
❏ Tree structured feature
Continuous quantitative features
Continuous quantitative feature
Discrete quantitative feature
Ordinal qualitative feature
Nominal qualitative feature
Tree structured feature
Examples: height, weight, blood pressure, speed, etc.
Single numeric value
Interval
Fuzzy
Discrete quantitative features
Continuous quantitative feature
Discrete quantitative feature
Ordinal qualitative feature
Nominal qualitative feature
Tree structured feature
Examples:
● Number of cities in a state
● Number of family members
Ordinal qualitative features
Continuous quantitative feature
Discrete quantitative feature
Ordinal qualitative feature
Nominal qualitative feature
Tree structured feature
{Junior high
1
High school
2
Undergrad
3
Graduate school}
4
Nominal qualitative features
Tree structured features
Continuous quantitative feature
Discrete quantitative feature
Ordinal qualitative feature
Nominal qualitative feature
Tree structured feature
Continuous quantitative feature
Discrete quantitative feature
Ordinal qualitative feature
Nominal qualitative feature
Tree structured feature
0
A
Example 1
Microprocessor
Male
B
Female
Motorola
Others
Intel
AB
68020
Tree structured features
Continuous quantitative feature
Discrete quantitative feature
Ordinal qualitative feature
Nominal qualitative feature
Tree structured feature
Example 2
68030
68040
80386
80486
Pentium
HP
Distance: A basic definition
d (A, B) = || . || defined as, for example:
Clothes
Outwear
Jackets
Footwear
Shirt
• Length
• Cardinality
Shoes Hiking boots
Skipants
Distance: An example
Distance: An example
A
B
-1
-4
A
5
1
B
|| A ⊕ B||
A
a
B
b
A= {a, b|
||A ⊗ B||
c
d
e
B = {b, c, d}
|| A ⊕ B || = card({a, b, c, d, e}) = 5
|| A ⊗ B || = card ({ b}) = 1
Z80
Related documents