1. BUS 297D: Data Mining
Professor David Mease
Lecture 1
Agenda:
1) Go over information on course web page
2) Lecture over Chapter 1
3) Discuss necessary software
4) Lecture over Chapter 2

2. BUS 297D: Data Mining Professor David Mease
Course web page and greensheet:
This page is linked from my SJSU web page
It is also linked from my personal page
which is easily found by querying “David Mease” or simply “Mease” on any search engine

3. Introduction to Data Mining Chapter 1: Introductionby
Tan, Steinbach, Kumar
Chapter 1: Introduction

4. What is Data Mining?
Data mining is the process of automatically discovering useful information in large data repositories. (page 2)
There are many other definitions

5. In class exercise #1:
Find a different definition of data mining online. How does it compare to the one in the text on the previous slide?

6. Data Mining Examples and Non-Examples
-Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)
-Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com, etc.)
NOT Data Mining:
-Look up phone number in phone directory
-Query a Web search engine for information about “Amazon”

7. Why Mine Data? Scientific Viewpoint
Data collected and stored at enormous speeds (GB/hour)
remote sensors on a satellite
telescopes scanning the skies
microarrays generating gene expression data
scientific simulations generating terabytes of data
Traditional techniques infeasible for raw data
Data mining may help scientists
in classifying and segmenting data
in hypothesis formation

8. Why Mine Data? Commercial Viewpoint
Lots of data is being collected and warehoused
Web data, e-commerce
Purchases at department/ grocery stores
Bank/credit card transactions
Computers have become cheaper and more powerful
Competitive pressure is strong
Provide better, customized services for an edge

9. In class exercise #2:
Give an example of something you did yesterday or today which resulted in data which could potentially be mined to discover useful information.

10. Origins of Data Mining (page 6)
Draws ideas from machine learning, AI, pattern recognition and statistics
Traditional techniques may be unsuitable due to
Enormity of data
High dimensionality of data
Heterogeneous, distributed nature of data
AI/Machine Learning/
Pattern Recognition
Statistics
Data Mining

11. 2 Types of Data Mining Tasks (page 7)
Prediction Methods:
Use some variables to predict unknown or future values of other variables.
Description Methods:
Find human-interpretable patterns that describe the data.

12. Examples of Data Mining Tasks
Classification [Predictive] (Chapters 4,5)
Regression [Predictive] (covered in stats classes)
Visualization [Descriptive] (in Chapter 3)
Association Analysis [Descriptive] (Chapter 6)
Clustering [Descriptive] (Chapter 8)
Anomaly Detection [Descriptive] (Chapter 10)

13. Software We Will Use:
You should make sure you have access to the following two software packages for this course
Microsoft Excel R
Can be downloaded from
for Windows, Mac or Linux

14. Downloading R for Windows:

15. Downloading R for Windows:

16. Downloading R for Windows:

17. Introduction to Data Miningby
Tan, Steinbach, Kumar
Chapter 2: Data

18. What is Data? 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, instance, or observation
Attributes
Objects

19. Reading Data into Excel
Download it from the web at
Then right click on it and select “Open With” then “Choose Program”

20. Reading Data into Excel
Choose Excel
Oops! Everything is in the first column!
Solution: click on
the first column
then select
“Data” then
“Text to Columns”

21. Reading Data into Excel
Choose “Delimited” and hit “Next”

22. Reading Data into Excel
Check only “Space” then “Next” again

23. Reading Data into Excel
Q: Why did this work? Why don’t all spaces cause column splits?

24. Reading Data into Excel
Q: Why did this work? Why don’t all spaces cause column splits?
A: The file is escaped using quotes.
(read for more information)

25. Reading Data into R Download it from the web at
Set your working directory:
setwd("C:/Documents and Settings/David/Desktop")
Read it in:
data<-read.csv("stats202log.txt",
sep=" ",header=F)

26. Reading Data into R Look at the first 5 rows: data[1:5,]
V1 V2 V V4 V V6 V7 V V9
[19/Jun/2007:00:31: ] GET / HTTP/
[19/Jun/2007:00:31: ] GET /mease.jpg HTTP/
[19/Jun/2007:00:31: ] GET /favicon.ico HTTP/
[19/Jun/2007:02:50: ] GET / HTTP/
[19/Jun/2007:02:50: ] GET /mease.jpg HTTP/
V V11 V12
Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; .NET CLR ) -
Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; .NET CLR ) -
Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; .NET CLR ) -
Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv: ) Gecko/ Firefox/
Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv: ) Gecko/ Firefox/

27. Reading Data into R Look at the first column: data[,1]
[1] …
[1901]
[1911]
[1921]
73 Levels:

28. Experimental vs. Observational Data (Important but not in book)
Experimental data describes data which was collected by someone who exercised strict control over all attributes.
Observational data describes data which was collected with no such controls. Most all data used in data mining is observational data so be careful.
Examples:
-Diet Coke vs. Weight
-Carbon Dioxide in Atmosphere vs.
Earth’s Temperature

29. Qualitative vs. Quantitative (P. 26)
Types of Attributes:
Qualitative vs. Quantitative (P. 26)
Qualitative (or Categorical) attributes represent distinct categories rather than numbers. Mathematical operations such as addition and subtraction do not make sense. Examples:
eye color, letter grade, IP address, zip code
Quantitative (or Numeric) attributes are numbers and can be treated as such. Examples:
weight, failures per hour, number of TVs, temperature

30. Types of Attributes (P. 25):
All Qualitative (or Categorical) attributes are either Nominal or Ordinal.
Nominal = categories with no order
Ordinal = categories with a meaningful order
All Quantitative (or Numeric) attributes are either Interval or Ratio.
Interval = no “true” zero, division makes no sense
Ratio = true zero exists, division makes sense

31. Types of Attributes: Some examples: Nominal
Examples: ID numbers, eye color, zip codes
Ordinal
Examples: letter grades, height in {tall, medium, short}
Interval
Examples: calendar dates, temperatures in Celsius or Fahrenheit, GMAT score
Ratio
Examples: temperature in Kelvin, length, time, counts

32. Properties of Attribute Values
The type of an attribute depends on which of the following properties it possesses:
Distinctness: = 
Order: < >
Addition and subtraction:
Multiplication and division: * /
Nominal attribute: distinctness
Ordinal attribute: distinctness & order
Interval attribute: distinctness, order & addition
Ratio attribute: all 4 properties

33. Discrete vs. Continuous (P. 28)
Discrete Attribute
Has only a finite or countably infinite set of values
Examples: zip codes, counts, or the set of words in a collection of documents
Often represented as integer variables
Note: binary attributes are a special case of discrete attributes which have only 2 values
Continuous Attribute
Has real numbers as attribute values
Can compute as accurately as instruments allow
Examples: temperature, height, or weight
Practically, real values can only be measured and represented using a finite number of digits
Continuous attributes are typically represented as floating-point variables

34. Discrete vs. Continuous (P. 28)
Qualitative (categorical) attributes are always discrete
Quantitative (numeric) attributes can be either discrete or continuous

35. In class exercise #3:
Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or ratio). Some cases may have more than one interpretation, so briefly indicate your reasoning if you think there may be some ambiguity.
a) Number of telephones in your house
b) Size of French Fries (Medium or Large or X-Large)
c) Ownership of a cell phone
d) Number of local phone calls you made in a month
e) Length of longest phone call
f) Length of your foot
g) Price of your textbook
h) Zip code
i) Temperature in degrees Fahrenheit
j) Temperature in degrees Celsius
k) Temperature in Kelvins

36. Types of Data in R
R often distinguishes between qualitative (categorical) attributes and quantitative (numeric)
In R,
qualitative (categorical) = “factor”
quantitative (numeric) = “numeric”

37. Types of Data in R
For example, the IP address in the first column of is a factor
> data<-read.csv("stats202log.txt",
sep=" ",header=F)
> data[,1]
[1] …
[1901]
[1911]
[1921]
73 Levels:
> is.factor(data[,1])
[1] TRUE
> data[,1]+10
[1] NA NA NA NA NA NA NA NA …
Warning message:
+ not meaningful for factors …

38. Types of Data in R
However, the 8th column looks like it should be numeric. Why is it not? How do we fix this?
> data[,8]
[1] …
[1901]
[1921]
Levels:
> is.factor(data[,8])
[1] TRUE
> is.numeric(data[,8])
[1] FALSE

39. Types of Data in R
A: We should have told R that “-” means missing when we read it in.
> data<-read.csv("stats202log.txt",
sep=" ",header=F, na.strings = "-")
> is.factor(data[,8])
[1] FALSE
> is.numeric(data[,8])
[1] TRUE

40. Types of Data in R
Q: How would we create an attribute giving the following zip codes 94550, 00123, for three observations in R?

41. Types of Data in R
Q: How would we create an attribute giving the following zip codes 94550, 00123, for three observations in R?
A: Use quotes:
> zip_codes<- as.factor(c("94550","00123","43614"))

42. Types of Data in Excel
Excel is not quite as picky and allows you to mix types more
Also, you can change between a lot of different predefined formats in Excel by right clicking a column and then selecting “Format Cells” and looking under the “Number” tab

43. Types of Data in Excel
Q: How would we create an attribute giving the following zip codes 94550, 00123, for three observations in Excel?

44. Types of Data in Excel
Q: How would we create an attribute giving the following zip codes 94550, 00123, for three observations in Excel?
A: Right click on the column then choose “Format Cells” then under the “Number” tab select “Text”

45. Working with Data in R Creating Data: Some simple operations:
> aa<-c(1,10,12)
> aa
[1]
Some simple operations:
> aa+10
[1]
> length(aa)
[1] 3

46. Working with Data in R Creating More Data: > bb<-c(2,6,79)
> my_data_set<-data.frame(attributeA=aa,attributeB=bb)
> my_data_set
attributeA attributeB

47. Working with Data in R Indexing Data: > my_data_set[,1] [1] 1 10 12
[1]
> my_data_set[1,]
attributeA attributeB
> my_data_set[3,2]
[1] 79
> my_data_set[1:2,]

48. Working with Data in R Indexing Data: Arithmetic:
> my_data_set[c(1,3),]
attributeA attributeB
Arithmetic:
> aa/bb
[1]

49. Working with Data in R Summary Statistics: > mean(my_data_set[,1])
[1]
> median(my_data_set[,1])
[1] 10
> sqrt(var(my_data_set[,1]))
[1]

50. Working with Data in R Writing Data: Help!:
> setwd("C:/Documents and Settings/David/Desktop")
> write.csv(my_data_set,"my_data_set_file.csv")
Help!:
> ?write.csv