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Data Mining: Data
Lecture Notes for Chapter 2 Introduction to Data Mining
by Tan, Steinbach, Kumar
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
What is Data?
Collection of data objects and their attributes An attribute is a property or characteristic of an object
– Examples: eye color of a person, temperature, etc. – Attribute is also known as variable, field, characteristic, or feature Objects
Attributes
Tid Refund Marital Status 1 2 3 4 5 6 7 8 9 10
10
Taxable Income Cheat 125K 100K 70K 120K No No No No Yes No No Yes No Yes
Yes No No Yes No No Yes No No No
Single Married Single Married
Divorced 95K Married 60K
A collection of attributes describe an object
– Object is also known as record, point, case, sample, entity, or instance
Divorced 220K Single Married Single 85K 75K 90K
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
2
Attribute Values
Attribute values are numbers or symbols assigned to an attribute Distinction between attributes and attribute values
– Same attribute can be mapped to different attribute values
Example: height can be measured in feet or meters
– Different attributes can be mapped to the same set of values
Example: Attribute values for ID and age are integers But properties of attribute values can be different – ID has no limit but age has a maximum and minimum value
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3
Measurement of Length
The way you measure an attribute is somewhat may not match the attributes properties.
5 A B 7 C 8 3 2 1
D 10 4
E 15 5
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
4
Types of Attributes
There are different types of attributes
– Nominal
Examples: ID numbers, eye color, zip codes
– Ordinal
Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short}
– Interval
Examples: calendar dates, temperatures...