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CES 514 – Data Mining
Spring 2010
Sonoma State University
Course Details:
 Instructor: Bala Ravikumar (Ravi)
 Email: [email protected], [email protected]
 Tel: (707) 664 3335
 Office: Darwin Hall 116 I
 Course Web Page
http://ravi.cs.sonoma.edu/~ravi/ces514sp10
 Lecture time:
 6 to 8:45 PM, Wednesdays
 Room: Salazar Hall 2003
 Office hours: M 9 – 10, T 11 – 12, W 5 – 6
Prerequisites
 basic probability and statistics (probability distribution,
random variable, conditional probability etc.)
 algorithms and data structures (sorting, hashing, binary
trees, algorithm design techniques)
 Programming in high-level language (Java, Python, Matlab,
c#, …)
 Linear algebra (vectors, linear independence, matrix rank,
Gaussian elimination etc.)
These topics will be reviewed. However, it will be helpful
to spend some time on your own to familiarize yourself.
Text book
Christopher D. Manning, Prabhakar
Raghavan and Hinrich Schütze, Introduction to
Information Retrieval, Cambridge University
Press. 2008.
This book’s focus is on WEB DATA MINING
Web site for the text:
http://nlp.stanford.edu/IR-book/information-retrieval-book.html
Additional references
 Mining the Web, S.Chakrabarti, MKP.
 Data Mining, Witten and Frank, MKP.
 The elements of statistical learning, Hastie, Tibshirani,
and Friedman, Springer-Verlag.
 Web Data Mining: Exploring Hyperlinks, Contents and
Usage data, Bing Liu, Springer-Verlag.
 Introduction to Data Mining, Pang-Ning Tan, Michael
Steinbach, and Vipin Kumar, Pearson/Addison Wesley.
Overlapping fields
Statistics
 Artificial intelligence (machine learning)
 Data base and Information retrieval
 Natural language processing
 Algorithm design and analysis

Grading
 Quiz: 10%
 Home Work: 25 %
 Midterm: 15%
 One mid-term, in-class, open book/notes?
 Final Exam: 25%
 In-class or take-home?
 Project: 25%
 Individual, design and implementation
Example Projects from Fall 2005 and 2007
Strategy for predicting the winner in a game similar to Jai Alai.
 Hand-written character recognition
 classify the type of disease based on some test results
 classification of e-mail (junk vs. useful, personal vs. business vs. family etc.)
 classification of questions in a multiple choice test based on the responses of
students
 identifying the author from a sample text
 implement an association rule mining algorithm
 implement a visualization algorithm that provides various options for viewing
the data
 classifying mushroom into edible and poisonous based on a number of
attributes – such as color, length of the stem, width etc.
 classifying web site based on content

Project is done individually, and is semester long - implement,
test, write a paper, present in class.
Today’s lecture
Overview of the course
 Chapter 1 of the text

Overview of Topics
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Web data organization
Web search
Classification (supervised learning)
Clustering (unsupervised learning)
Association rule mining
Language models for information retrieval
Vector space models
SVM and other tools
LSI and tools from linear algebra
Link analysis
Other applications – e.g. bioinformatics
What is data mining?
 Data mining is also called knowledge discovery
 Data mining is
 extraction of useful patterns from data sources, e.g.,
databases, texts, web, images, etc.
 Patterns must be:
 valid, novel, potentially useful, understandable
 Our focus will be on text data (in particular web)
Some sample problems in Data Mining
Extract useful knowledge from the vast data and information
available on the web. (e.g. tagging of web sites, labeling images,
predict the needs of a web surfer from pattern of clicks.)
 Using the financial record of a person, determine the risk
involved in giving a loan. (decision could be yes or no. more
generally, it could be the type of loan – interest rate, duration etc.)
 movie (book etc.) recommendation based on prior choices.
 prediction of weather, traffic pattern, outcome of an event etc.
 From the items recorded in the check-out counter of a super
market, determine any correlation between items being sold. (used
to decide which ones to put on sale.)
 study and understand of social networks.
 rank web page according to significance.

Classic data mining tasks
 Classification
mining patterns that can classify future (new) data into known
classes.
 Association rule mining
mining any rule of the form X  Y, where X and Y are sets of
data items.
 Clustering
identifying similar groups in the data
 Regression analysis
Classic data mining tasks (contd)
 Sequential pattern mining:
A sequential rule: A B, says that event A will be immediately
followed by event B with a certain confidence
 Deviation detection:
discovering the most significant changes in data
 Data visualization: using graphical methods to show
patterns in data.
Why is data mining important?
 Computerization of businesses produce huge amount
of data
 How to make best use of data?
 Knowledge discovered from data can be used for
competitive advantage.
 Online businesses generate even larger data sets
 Online retailers (e.g., amazon.com) are largely
driven by data mining.
 Web search engines are information retrieval and
data mining companies
Why is data mining necessary?
 Make use of your data assets
 There is a big gap from stored data to knowledge;
and the transition won’t occur automatically.
 Many interesting things you want to find cannot be
found using database queries
“find me people likely to buy my products”
“Who are likely to respond to my promotion?”
“Which movies should be recommended to each customer?”
Why data mining now?

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The data is abundant.
The computing power is not an issue.
Data mining tools are available
The competitive pressure is very strong.
 Almost every company is doing (or has to do) it
 Socio-political exigencies
 Detecting terrorism activities
 New technologies
 Streaming data, mobile computing, wireless networks
Related fields
 Data mining is an multi-disciplinary field:

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Machine learning/artificial intelligence
Statistics
Databases
Information retrieval
Visualization
Natural language processing
Game theory
etc.
Data mining applications
 Marketing: customer profiling and retention, identifying





potential customers, market segmentation.
Engineering: identify causes of problems in products.
Scientific data analysis: weather prediction, financial data
analysis, image analysis etc.
Fraud detection: identifying credit card fraud, intrusion
detection.
Text and web: a huge number of applications …
Bioinformatics: structure prediction, classification, microarray
analysis etc.
 Any application that involves a large amount of data …
Structural descriptions
 Example: if-then rules
If tear production rate = reduced
then recommendation = none
Otherwise, if age = young and astigmatic = no
then recommendation = soft
Age
Spectacle
prescription
Astigmatism
Tear
production
rate
Recommended
lenses
Young
Myope
No
Reduced
None
Young
Hypermetrope
No
Normal
Soft
Prepresbyopic
Hypermetrope
No
Reduced
None
Presbyopic
Myope
Yes
Normal
Hard
…
…
…
…
…
Classification vs. association rules
 Classification rule:
predicts value of a given attribute (the classification of
an example)
If outlook = sunny and humidity = high
then play = no
 Association rule:
predicts value of arbitrary attribute (or combination)
If temperature = cool then humidity = normal
If humidity = normal and windy = false
then play = yes
If outlook = sunny and play = no
then humidity = high
If windy = false and play = no
then outlook = sunny and humidity = high
A decision tree for this problem
Predicting CPU performance
 Example: 209 different computer configurations
Cycle time
(ns)
Main memory
(Kb)
Cache
(Kb)
Channels
Performance
MYCT
MMIN
MMAX
CACH
CHMIN
CHMAX
PRP
1
125
256
6000
256
16
128
198
2
29
8000
32000
32
8
32
269
208
480
512
8000
32
0
0
67
209
480
1000
4000
0
0
0
45
…
 Linear regression function
PRP =
-55.9 + 0.0489 MYCT + 0.0153 MMIN + 0.0056 MMAX
+ 0.6410 CACH - 0.2700 CHMIN + 1.480 CHMAX
Spam filter software
Given below are the % of occurrences of a few select
words in spam and genuine e-mail messages:
A decision list may be used to identify spam.
Text mining
 Data mining on text
 Due to a huge amount of online texts on the Web and other
sources
 Text contains a huge amount of information of any
imaginable type!
 A major direction and tremendous opportunity!
 Main topics



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Text classification and clustering
Information retrieval
Information extraction
Opinion mining and summarization
Example: Opinion Mining
 The Web has dramatically changed the way that
people express their opinions.
 One can post their opinions on almost anything at
review sites, Internet forums, discussion groups,
blogs, etc.
 Product reviews
 Benefits of Review Analysis
 Potential Customer: No need to read many reviews
 Product manufacturer: market intelligence, product
benchmarking
Feature Based Analysis & Summarization
 Extracting product features (called Opinion Features)
that have been commented on by customers.
 Identifying opinion sentences in each review and
deciding whether each opinion sentence is positive
or negative.
 Summarizing and comparing results.
An example
GREAT Camera., Jun 3, 2004
Reviewer: jprice174 from Atlanta, Ga.
I did a lot of research last year
before I bought this camera... It
kinda hurt to leave behind my
beloved nikon 35mm SLR, but I
was going to Italy, and I needed
something smaller, and digital.
The pictures coming out of this
camera are amazing. The 'auto'
feature takes great pictures most
of the time. And with digital,
you're not wasting film if the
picture doesn't come out. …
….
Summary:
Feature1: picture
Positive: 12
 The pictures coming out of this
camera are amazing.
 Overall this is a good camera with a
really good picture clarity.
....
Negative: 2
 The pictures come out hazy if your
hands shake even for a moment
during the entire process of taking a
picture.
 Focusing on a display rack about 20
feet away in a brightly lit room
during day time, pictures produced
by this camera were blurry and in a
shade of orange.
Feature2: battery life
Visual Comparison

Summary of
reviews of Digital
camera 1
+
_
Picture

Comparison of
reviews of
+
Digital camera 1
Digital camera 2
_
Battery
Zoom
Size
Weight
Information retrieval – Ch 1 Boolean query
Information Retrieval (IR) is finding material
(usually documents) of an unstructured nature
(usually text) that satisfies an information
need from within large collections (usually
stored on computers).
Sec. 1.1
Unstructured data in 1680
 Which plays of Shakespeare contain the words Brutus
AND Caesar but NOT Calpurnia?
 One could grep all of Shakespeare’s plays for Brutus
and Caesar, then strip out lines containing Calpurnia?
 Why is that not the answer?
 Slow (for large corpora)
 NOT Calpurnia is non-trivial
 Other operations (e.g., find the word Romans near
countrymen) not feasible
 Ranked retrieval (best documents to return)
 Later lectures
31
Sec. 1.1
Term-document incidence
Antony and Cleopatra
Julius Caesar
The Tempest
Hamlet
Othello
Macbeth
Antony
1
1
0
0
0
1
Brutus
1
1
0
1
0
0
Caesar
1
1
0
1
1
1
Calpurnia
0
1
0
0
0
0
Cleopatra
1
0
0
0
0
0
mercy
1
0
1
1
1
1
worser
1
0
1
1
1
0
Brutus AND Caesar BUT NOT
Calpurnia
1 if play contains
word, 0 otherwise
Sec. 1.1
Incidence vectors
 So we have a 0/1 vector for each term.
 To answer query: take the vectors for Brutus, Caesar
and Calpurnia (complemented)  bitwise AND.
 110100 AND 110111 AND 101111 = 100100.
33