chapter1_Introduction.pdf data mining ppt

Data Mining: Introduction
Chapter 1
Introduction to Data Mining
by
Tan, Steinbach, Karpatne, Kumar
1

Large-scale Data is Everywhere!
 There has been enormous data
growth in both commercial and
scientific databases due to
advances in data generation
and collection technologies
 New mantra
 Gather whatever data you can
whenever and wherever
possible.
 Expectations
 Gathered data will have value
either for the purpose
collected or for a purpose not
envisioned.
Computational Simulations
Social Networking: Twitter
Sensor Networks
Traffic Patterns
Cyber Security
2
E-Commerce

Why Data Mining? Commercial Viewpoint
 Lots of data is being collected
and warehoused
– Web data
Google has Peta Bytes of web data
Facebook has billions of active users
– purchases at department/
grocery stores, e-commerce
 Amazon handles millions of visits/day
– Bank/Credit Card transactions
 Computers have become cheaper and more powerful
 Competitive Pressure is Strong
– Provide better, customized services for an edge (e.g. in
Customer Relationship Management)
3

Why Data Mining? Scientific Viewpoint
 Data collected and stored at
enormous speeds
– remote sensors on a satellite
 NASA EOSDIS archives over
petabytes of earth science data / year
– telescopes scanning the skies
 Sky survey data
– High-throughput biological data
– scientific simulations
 terabytes of data generated in a few hours
 Data mining helps scientists
– in automated analysis of massive datasets
– In hypothesis formation
4
fMRI Data from Brain Sky Survey Data
Gene Expression Data
Surface Temperature of Earth

Great opportunities to improve productivity in all walks of life
5

Great Opportunities to Solve Society’s Major Problems
Improving health care and reducing costs
Finding alternative/ green energy sources
Predicting the impact of climate change
Reducing hunger and poverty by
increasing agriculture production
6

What is Data Mining?
 Many Definitions
– Non-trivial extraction of implicit, previously unknown
and potentially useful information from data
– Exploration & analysis, by automatic or semi-automatic
means, of large quantities of data in order to discover
meaningful patterns
7

 Draws ideas from machine learning/AI, pattern recognition,
statistics, and database systems
 Traditional techniques may be unsuitable due to data that is
– Large-scale
– High dimensional
– Heterogeneous
– Complex
– Distributed
 A key component of the emerging field of data science and data-
driven discovery
Origins of Data Mining
8

Data Mining Tasks
 Prediction Methods
– Use some variables to predict unknown or
future values of other variables.
 Description Methods
– Find human-interpretable patterns that
describe the data.
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
9

Tid Refund Marital
Status
Taxable
Income Cheat
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes
12 Yes Divorced 220K No
10
Milk
Data
Data Mining Tasks …
10

 Find a model for class attribute as a function of
the values of other attributes
Tid Employed
Level of
Education
# years at
present
address
Credit
Worthy
1 Yes Graduate 5 Yes
2 Yes High School 2 No
3 No Undergrad 1 No
4 Yes High School 10 Yes
… … … … …
10
Model for predicting credit
worthiness
Class Employed
No Education
Number of
years
No Yes
Graduate
{ High school,
Undergrad }
Yes No
> 7 yrs < 7 yrs
Yes
Number of
years
No
> 3 yr < 3 yr
Predictive Modeling: Classification
11

Classification Example
Test
Set
Training
Set
Model
Learn
Classifier
Tid Employed
Level of
Education
# years at
present
address
Credit
Worthy
1 Yes Graduate 5 Yes
2 Yes High School 2 No
3 No Undergrad 1 No
4 Yes High School 10 Yes
… … … … …
10
Tid Employed
Level of
Education
# years at
present
address
Credit
Worthy
1 Yes Undergrad 7 ?
2 No Graduate 3 ?
3 Yes High School 2 ?
… … … … …
10
12

 Classifying credit card transactions
as legitimate or fraudulent
 Classifying land covers (water bodies, urban areas,
forests, etc.) using satellite data
 Categorizing news stories as finance,
weather, entertainment, sports, etc
 Identifying intruders in the cyberspace
 Predicting tumor cells as benign or malignant
 Classifying secondary structures of protein
as alpha-helix, beta-sheet, or random coil
Examples of Classification Task
13

Classification: Application 1
 Fraud Detection
– Goal: Predict fraudulent cases in credit card
transactions.
– Approach:
 Use credit card transactions and the information
on its account-holder as attributes.
– When does a customer buy, what does he buy, how
often he pays on time, etc
 Label past transactions as fraud or fair
transactions. This forms the class attribute.
 Learn a model for the class of the transactions.
 Use this model to detect fraud by observing credit
card transactions on an account.
14

 Churn prediction for telephone customers
– Goal: To predict whether a customer is likely
to be lost to a competitor.
– Approach:
 Use detailed record of transactions with each of the
past and present customers, to find attributes.
– How often the customer calls, where he calls, what time-
of-the day he calls most, his financial status, marital
status, etc.
 Label the customers as loyal or disloyal.
 Find a model for loyalty.
From [Berry & Linoff] Data Mining Techniques, 1997
15

 Sky Survey Cataloging
– Goal: To predict class (star or galaxy) of sky objects,
especially visually faint ones, based on the telescopic
survey images (from Palomar Observatory).
– 3000 images with 23,040 x 23,040 pixels per image.
– Approach:
 Segment the image.
 Measure image attributes (features) - 40 of them per
object.
 Model the class based on these features.
 Success Story: Could find 16 new high red-shift
quasars, some of the farthest objects that are difficult
to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
16

Classifying Galaxies
Early
Intermediate
Late
Data Size:
• 72 million stars, 20 million galaxies
• Object Catalog: 9 GB
• Image Database: 150 GB
Class:
• Stages of Formation
Attributes:
• Image features,
• Characteristics of light
waves received, etc.
Courtesy: http://aps.umn.edu
17

Regression
 Predict a value of a given continuous valued variable
based on the values of other variables, assuming a
linear or nonlinear model of dependency.
 Extensively studied in statistics, neural network fields.
 Examples:
– Predicting sales amounts of new product based on
advetising expenditure.
– Predicting wind velocities as a function of
temperature, humidity, air pressure, etc.
– Time series prediction of stock market indices.
18

 Finding groups of objects such that the objects in a
group will be similar (or related) to one another and
different from (or unrelated to) the objects in other
groups
Inter-cluster
distances are
maximized
Intra-cluster
distances are
minimized
Clustering
19

 Understanding
– Custom profiling for targeted
marketing
– Group related documents for
browsing
– Group genes and proteins that
have similar functionality
– Group stocks with similar price
fluctuations
 Summarization
– Reduce the size of large data
sets
Applications of Cluster Analysis
Clusters for Raw SST and Raw NPP
longitude
latitude
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
90
60
30
0
-30
-60
-90
Cluster
Sea Cluster 1
Sea Cluster 2
Ice or No NPP
Land Cluster 1
Land Cluster 2
Use of K-means to
partition Sea Surface
Temperature (SST) and
Net Primary Production
(NPP) into clusters that
reflect the Northern
and Southern
Hemispheres.
Courtesy: Michael Eisen
20

Clustering: Application 1
 Market Segmentation:
– Goal: subdivide a market into distinct subsets of
customers where any subset may conceivably be
selected as a market target to be reached with a
distinct marketing mix.
– Approach:
 Collect different attributes of customers based on
their geographical and lifestyle related information.
 Find clusters of similar customers.
 Measure the clustering quality by observing buying
patterns of customers in same cluster vs. those
from different clusters.
21

Clustering: Application 2
 Document Clustering:
– Goal: To find groups of documents that are similar to
each other based on the important terms appearing in
them.
– Approach: To identify frequently occurring terms in
each document. Form a similarity measure based on
the frequencies of different terms. Use it to cluster.
22
Enron email dataset

Association Rule Discovery: Definition
 Given a set of records each of which contain
some number of items from a given collection
– Produce dependency rules which will predict
occurrence of an item based on occurrences of other
items.
TID Items
1 Bread, Coke, Milk
2 Beer, Bread
3 Beer, Coke, Diaper, Milk
4 Beer, Bread, Diaper, Milk
5 Coke, Diaper, Milk
Rules Discovered:
{Milk} --> {Coke}
{Diaper, Milk} --> {Beer}
Rules Discovered:
{Milk} --> {Coke}
{Diaper, Milk} --> {Beer}
23

Association Analysis: Applications
 Market-basket analysis
– Rules are used for sales promotion, shelf
management, and inventory management
 Telecommunication alarm diagnosis
– Rules are used to find combination of alarms that
occur together frequently in the same time period
 Medical Informatics
– Rules are used to find combination of patient
symptoms and test results associated with certain
diseases
24

 An Example Subspace Differential Coexpression Pattern
from lung cancer dataset
Enriched with the TNF/NFB signaling pathway
which is well-known to be related to lung cancer
P-value: 1.4*10-5 (6/10 overlap with the pathway)
[Fang et al PSB 2010]
Three lung cancer datasets [Bhattacharjee et a
2001], [Stearman et al. 2005], [Su et al. 2007]
Association Analysis: Applications
25

Deviation/Anomaly/Change Detection
 Detect significant deviations from
normal behavior
 Applications:
– Credit Card Fraud Detection
– Network Intrusion
Detection
– Identify anomalous behavior from
sensor networks for monitoring and
surveillance.
– Detecting changes in the global
forest cover.
26

Motivating Challenges
 Scalability
 High Dimensionality
 Heterogeneous and Complex Data
 Data Ownership and Distribution
 Non-traditional Analysis
27

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