Download data mining

Data Mining
Chapter 26
1
Chapter 1. Introduction

Motivation: Why data mining?

What is data mining?

Data Mining: On what kind of data?

Data mining functionality

Are all the patterns interesting?

Major issues in data mining
2
Motivation: “Necessity is the
Mother of Invention”

Data explosion problem

Automated data collection tools and mature database technology
lead to tremendous amounts of data stored in databases, data
warehouses and other information repositories

We are drowning in data, but starving for knowledge!

Solution: Data warehousing and data mining

Data warehousing and on-line analytical processing

Extraction of interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases
3
Evolution of Database Technology

1960s:


1970s:


Relational data model, relational DBMS implementation
1980s:


Data collection, database creation, IMS and network DBMS
RDBMS, advanced data models (extended-relational, OO,
deductive, etc.) and application-oriented DBMS (spatial, scientific,
engineering, etc.)
1990s—2000s:

Data mining and data warehousing, multimedia databases, and
Web databases
4
What Is Data Mining?

Data mining (knowledge discovery in databases):


Alternative names:



Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) information or patterns
from data in large databases
Data mining: a misnomer?
Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.
What is not data mining?


(Deductive) query processing.
Expert systems or small ML/statistical programs
5
Why Data Mining? — Potential
Applications

Database analysis and decision support

Market analysis and management


Risk analysis and management



target marketing, customer relation management, market
basket analysis, cross selling, market segmentation
Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
Fraud detection and management
Other Applications




Text mining (news group, email, documents)
Stream data mining
Web mining.
DNA data analysis
6
Market Analysis and Management (1)

Where are the data sources for analysis?


Target marketing


Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over time


Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
Conversion of single to a joint bank account: marriage, etc.
Cross-market analysis

Associations/co-relations between product sales

Prediction based on the association information
7
Market Analysis and Management (2)

Customer profiling

data mining can tell you what types of customers buy what
products (clustering or classification)


Identifying customer requirements

identifying the best products for different customers

use prediction to find what factors will attract new customers
Provides summary information

various multidimensional summary reports

statistical summary information (data central tendency and
variation)
8
Corporate Analysis and Risk
Management

Finance planning and asset evaluation




Resource planning:


cash flow analysis and prediction
contingent claim analysis to evaluate assets
cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
summarize and compare the resources and spending
Competition:



monitor competitors and market directions
group customers into classes and a class-based pricing
procedure
set pricing strategy in a highly competitive market
9
Fraud Detection and Management (1)

Applications


Approach


widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
use historical data to build models of fraudulent behavior and
use data mining to help identify similar instances
Examples



auto insurance: detect a group of people who stage accidents to
collect on insurance
money laundering: detect suspicious money transactions (US
Treasury's Financial Crimes Enforcement Network)
medical insurance: detect professional patients and ring of
doctors and ring of references
10
Fraud Detection and Management (2)

Detecting inappropriate medical treatment


Detecting telephone fraud



Australian Health Insurance Commission identifies that in many
cases blanket screening tests were requested (save Australian
$1m/yr).
Telephone call model: destination of the call, duration, time of
day or week. Analyze patterns that deviate from an expected
norm.
British Telecom identified discrete groups of callers with frequent
intra-group calls, especially mobile phones, and broke a
multimillion dollar fraud.
Retail

Analysts estimate that 38% of retail shrink is due to dishonest
employees.
11
Other Applications

Sports


Astronomy


IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
JPL and the Palomar Observatory discovered 22 quasars with
the help of data mining
Internet Web Surf-Aid

IBM Surf-Aid applies data mining algorithms to Web access logs
for market-related pages to discover customer preference and
behavior pages, analyzing effectiveness of Web marketing,
improving Web site organization, etc.
12
Data Mining: A KDD Process
Pattern Evaluation

Data mining: the core of
knowledge discovery
Data Mining
process.
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
13
Steps of a KDD Process

Learning the application domain:




Creating a target data set: data selection
Data cleaning and preprocessing: (may take 60% of effort!)
Data reduction and transformation:




summarization, classification, regression, association, clustering.
Choosing the mining algorithm(s)
Data mining: search for patterns of interest
Pattern evaluation and knowledge presentation


Find useful features, dimensionality/variable reduction, invariant
representation.
Choosing functions of data mining


relevant prior knowledge and goals of application
visualization, transformation, removing redundant patterns, etc.
Use of discovered knowledge
14
Data Mining: On What Kind of
Data?




Relational databases
Data warehouses
Transactional databases
Advanced DB and information repositories






Object-oriented and object-relational databases
Spatial and temporal data
Time-series data and stream data
Text databases and multimedia databases
Heterogeneous and legacy databases
WWW
15
Data Mining Functionalities
16
Association Rule Mining

Association rule mining:



Finding frequent patterns, associations, correlations, or causal
structures among sets of items or objects in transaction
databases, relational databases, and other information
repositories.
Frequent pattern: pattern (set of items, sequence, etc.) that
occurs frequently in a database
Motivation: finding regularities in data




What products were often purchased together? — Beer and
diapers?!
What are the subsequent purchases after buying a PC?
What kinds of DNA are sensitive to this new drug?
Can we automatically classify web documents?
17
Association Rule Mining (cont.)
Transaction-id
10
20
30
40
Customer
buys both
Customer
buys beer
Items bought
A, B, C
A, C
A, D
B, E, F
Customer
buys
diapers


Itemset X={x1, …, xk}
Find all the rules XY with min
confidence and support
 support, s, probability that a
transaction contains XY
 confidence, c, conditional
probability that a transaction
having X also contains Y.
Let min_support = 50%,
min_conf = 50%:
A  C (50%, 66.7%)
C  A (50%, 100%)
18
Mining Association Rules—an Example
Transaction-id
10
20
30
40
Items bought
A, B, C
A, C
A, D
B, E, F
Min. support 50%
Min. confidence 50%
Frequent pattern
{A}
{B}
{C}
{A, C}
Support
75%
50%
50%
50%
For rule A  C:
support = support({A}{C}) = 50%
confidence = support({A}{C})/support({A}) = 66.6%
19
Apriori: A Candidate Generation-and-test
Approach

Any subset of a frequent itemset must be frequent





if {beer, diaper, nuts} is frequent, so is {beer, diaper}
every transaction having {beer, diaper, nuts} also contains {beer, diaper}
Apriori pruning principle: If there is any itemset which is infrequent, its
superset should not be generated/tested!
Method:
 generate length (k+1) candidate itemsets from length k frequent
itemsets, and
 test the candidates against DB
The performance studies show its efficiency and scalability
20
The Apriori Algorithm — An Example
Database TDB
Tid
Items
10
A, C, D
20
B, C, E
30
A, B, C, E
40
B, E
L2
sup
{A}
2
{B}
3
{C}
3
{D}
1
{E}
3
C1
1st scan
C2
Itemset
sup
{A, C}
2
{B, C}
2
{B, E}
3
{C, E}
2
C3
Itemset
Itemset
{B, C, E}
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
sup
1
2
1
2
3
2
Itemset
sup
{A}
2
{B}
3
{C}
3
{E}
3
L1
C2
2nd scan
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
3rd scan
L3
Itemset
{B, C, E}
sup
2
21
The Apriori Algorithm

Pseudo-code:
Ck: Candidate itemset of size k
Lk : frequent itemset of size k
L1 = {frequent items};
for (k = 1; Lk !=; k++) do begin
Ck+1 = candidates generated from Lk;
for each transaction t in database do
increment the count of all candidates in Ck+1
that are contained in t
Lk+1 = candidates in Ck+1 with min_support
end
return k Lk;
22
Important Details of Apriori


How to generate candidates?

Step 1: self-joining Lk

Step 2: pruning
Example of Candidate-generation



L3={abc, abd, acd, ace, bcd}
Self-joining: L3*L3
 abcd from abc and abd
 acde from acd and ace
Pruning:
acde is removed because ade is not in L3
C4={abcd}


23
How to Generate Candidates?

Suppose the items in Lk-1 are listed in an order

Step 1: self-joining Lk-1
insert into Ck
select p.item1, p.item2, …, p.itemk-1, q.itemk-1
from Lk-1 p, Lk-1 q
where p.item1=q.item1, …, p.itemk-2=q.itemk-2, p.itemk-1 < q.itemk1

Step 2: pruning
forall itemsets c in Ck do
forall (k-1)-subsets s of c do
if (s is not in Lk-1) then delete c from Ck
24
Classification and Prediction




Finding models (functions) that describe and
distinguish classes or concepts for future prediction
E.g., classify countries based on climate, or classify
cars based on gas mileage
Presentation: decision-tree, classification rule, neural
network
Prediction: Predict some unknown or missing
numerical values
25
Classification Process: Model
Construction
Training
Data
NAME
M ike
M ary
B ill
Jim
D ave
A nne
RANK
YEARS TENURED
A ssistant P rof
3
no
A ssistant P rof
7
yes
P rofessor
2
yes
A ssociate P rof
7
yes
A ssistant P rof
6
no
A ssociate P rof
3
no
Classification
Algorithms
Classifier
(Model)
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’
26
Classification Process: Use the
Model in Prediction
Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
NAME
T om
M erlisa
G eorge
Joseph
RANK
YEARS TENURED
A ssistant P rof
2
no
A ssociate P rof
7
no
P rofessor
5
yes
A ssistant P rof
7
yes
Tenured?
27
Decision Trees
Training
set
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income
high
high
high
medium
low
low
low
medium
low
medium
medium
medium
high
medium
student
no
no
no
no
yes
yes
yes
no
yes
yes
yes
no
yes
no
credit_rating
fair
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
28
Output: A Decision Tree for
“buys_computer”
age?
<=30
student?
overcast
30..40
yes
>40
credit rating?
no
yes
excellent
fair
no
yes
no
yes
29
Cluster and outlier analysis

Cluster analysis



Class label is unknown: Group data to form new classes, e.g., cluster
houses to find distribution patterns
Clustering based on the principle: maximizing the intra-class similarity
and minimizing the interclass similarity
Outlier analysis

Outlier: a data object that does not comply with the general behavior of
the data

It can be considered as noise or exception but is quite useful in fraud
detection, rare events analysis
30
Clusters and Outliers
31