文档介绍：Data Mining: Data
Lecture Notes for Chapter 2
Introduction to Data Mining
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
A collection of attributes describe an object
Object is also known as record, point, case, sample, entity, or instance
Attributes
Objects
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
Measurement of Length
The way you measure an attribute is somewhat may not match the attributes properties.
Types of Attributes
There are different types of attributes
Nominal
Examples: ID numbers, eye color, zip codes
Ordinal
Examples: rankings (., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short}
Interval
Examples: calendar dates, temperatures in Celsius or Fahrenheit.
Ratio
Examples: temperature in Kelvin, length, time, counts
Properties of Attribute Values
The type of an attribute depends on which of the following properties it possesses:
Distinctness: =
Order: < >
Addition: + -
Multiplication: * /
Nominal attribute: distinctness
Ordinal attribute: distinctness & order
Interval attribute: distinctness, order & addition
Ratio attribute: all 4 properties
Attribute Type
Description
Examples
Operations
Nominal
The values of a nominal attribute are just different names, ., nominal attributes provide only enough information to distinguish one object from another. (=, )
zip codes, employee ID n