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Data Mining Chapter 26

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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

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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

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Evolution of Database Technology

1960s: Data collection, database creation, IMS and network DBMS

1970s: Relational data model, relational DBMS implementation

1980s: 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

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What Is Data Mining?

Data mining (knowledge discovery in databases):

Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases

Alternative names: 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

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Why Data Mining? — Potential Applications

Database analysis and decision support Market analysis and management

target marketing, customer relation management, market basket analysis, cross selling, market segmentation

Risk analysis and management 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

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Market Analysis and Management (1)

Where are the data sources for analysis? Credit card transactions, loyalty cards, discount coupons,

customer complaint calls, plus (public) lifestyle studies

Target marketing Find clusters of “model” customers who share the same

characteristics: interest, income level, spending habits, etc.

Determine customer purchasing patterns over time 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

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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)

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Corporate Analysis and Risk Management

Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio,

trend analysis, etc.) Resource planning:

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

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Fraud Detection and Management (1)

Applications widely used in health care, retail, credit card services,

telecommunications (phone card fraud), etc. Approach

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

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Fraud Detection and Management (2)

Detecting inappropriate medical treatment Australian Health Insurance Commission identifies that

in many cases blanket screening tests were requested (save Australian $1m/yr).

Detecting telephone fraud 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.

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Other Applications

Sports IBM Advanced Scout analyzed NBA game statistics

(shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat

Astronomy 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.

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Data Mining: A KDD Process

Data mining: the core of knowledge discovery process.

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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Steps of a KDD Process

Learning the application domain: relevant prior knowledge and goals of application

Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation:

Find useful features, dimensionality/variable reduction, invariant representation.

Choosing functions of data mining summarization, classification, regression, association,

clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation

visualization, transformation, removing redundant patterns, etc.

Use of discovered knowledge

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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

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Data Mining Functionalities

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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?

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Association Rule Mining (cont.)

Itemset X={x1, …, xk} Find all the rules XY with min

confidence and support support, s, probability that a

transaction contains XY 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%)

Customerbuys diapers

Customerbuys both

Customerbuys beer

Transaction-id Items bought

10 A, B, C

20 A, C

30 A, D

40 B, E, F

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Mining Association Rules—an Example

For rule A C:support = support({A}{C}) = 50%confidence = support({A}{C})/support({A}) =

66.6%

Min. support 50%Min. confidence 50%

Transaction-id Items bought

10 A, B, C

20 A, C

30 A, D

40 B, E, F

Frequent pattern Support

{A} 75%

{B} 50%

{C} 50%

{A, C} 50%

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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

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The Apriori Algorithm — An Example

Database TDB

1st scan

C1L1

L2

C2 C2

2nd scan

C3 L33rd scan

Tid Items

10 A, C, D

20 B, C, E

30 A, B, C, E

40 B, E

Itemset sup

{A} 2

{B} 3

{C} 3

{D} 1

{E} 3

Itemset sup

{A} 2

{B} 3

{C} 3

{E} 3

Itemset

{A, B}

{A, C}

{A, E}

{B, C}

{B, E}

{C, E}

Itemset sup

{A, B} 1

{A, C} 2

{A, E} 1

{B, C} 2

{B, E} 3

{C, E} 2

Itemset sup

{A, C} 2

{B, C} 2

{B, E} 3

{C, E} 2

Itemset

{B, C, E}Itemset sup

{B, C, E} 2

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The Apriori Algorithm

Pseudo-code:Ck: Candidate itemset of size kLk : 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 endreturn k Lk;

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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}

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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.itemk-1

Step 2: pruningforall 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

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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

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Classification Process: Model Construction

TrainingData

NAME RANK YEARS TENUREDMike Assistant Prof 3 noMary Assistant Prof 7 yesBill Professor 2 yesJim Associate Prof 7 yesDave Assistant Prof 6 noAnne Associate Prof 3 no

ClassificationAlgorithms

IF rank = ‘professor’OR years > 6THEN tenured = ‘yes’

Classifier(Model)

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Classification Process: Use the Model in Prediction

Classifier

TestingData

NAME RANK YEARS TENUREDTom Assistant Prof 2 noMerlisa Associate Prof 7 noGeorge Professor 5 yesJoseph Assistant Prof 7 yes

Unseen Data

(Jeff, Professor, 4)

Tenured?

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Decision Trees

age income student credit_rating<=30 high no fair<=30 high no excellent31…40 high no fair>40 medium no fair>40 low yes fair>40 low yes excellent31…40 low yes excellent<=30 medium no fair<=30 low yes fair>40 medium yes fair<=30 medium yes excellent31…40 medium no excellent31…40 high yes fair>40 medium no excellent

Training set

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Output: A Decision Tree for “buys_computer”

age?

overcast

student? credit rating?

no yes fairexcellent

<=30 >40

no noyes yes

yes

30..40

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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

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Clusters and Outliers