Data Mining

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...