Data Mining

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Introduction

Motivation: Why data mining?

What is data mining?

Data Mining: On what kind of data?

Data mining functionality

Are all the patterns interesting?

Classification of data mining systems

Data Mining Task Primitives

Integration of data mining system with a DB and DW System

Major issues in data mining

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 Why Data Mining?

The Explosive Growth of Data

Data collection and data availability
Automated data collection tools, database systems, Web,
computerized society
Major sources of abundant data
Business: Web, e-commerce, transactions, stocks,
Science: Remote sensing, bioinformatics, scientific
simulation,
Society and everyone: news, digital cameras,
We are drowning in data, but starving for knowledge!
Necessity is the mother of inventionData miningAutomated
analysis of massive data sets 3
 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.)
Application-oriented DBMS (spatial, scientific, engineering, etc.)
1990s:
Data mining, data warehousing, multimedia databases, and Web
databases
2000s
Stream data management and mining
Data mining and its applications
Web technology (XML, data integration) and global information
systems
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 What Is Data Mining?

Data mining (knowledge discovery from data)

Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) patterns or knowledge from
huge amount of data
Data mining: a misnomer?
Alternative names
Knowledge discovery (mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting, business
intelligence, etc.
Watch out: Is everything data mining?
Simple search and query processing
(Deductive) expert systems
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Why Data Mining?Potential Applications

Data analysis and decision support

Market analysis and management
Target marketing, customer relationship management
(CRM), market basket analysis, cross selling, market
segmentation
Risk analysis and management
Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
Fraud detection and detection of unusual patterns (outliers)
Other Applications
Text mining (news group, email, documents) and Web mining
Stream data mining
Bioinformatics and bio-data analysis
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Ex. 1: Market Analysis and Management
Where does the data come from?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
Cross-market analysisFind associations/co-relations between product
sales, & predict based on such association
Customer profilingWhat types of customers buy what products (clustering
or classification)
Customer requirement analysis
Identify the best products for different customers
Predict what factors will attract new customers
Provision of summary information
Multidimensional summary reports
Statistical summary information (data central tendency and variation)

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Ex. 2: Corporate Analysis & 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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Ex. 3: Fraud Detection & Mining Unusual
Patterns

Approaches: Clustering & model construction for frauds, outlier analysis

Applications: Health care, retail, credit card service, telecomm.
Auto insurance: ring of collisions
Money laundering: suspicious monetary transactions
Medical insurance
Professional patients, ring of doctors, and ring of references
Unnecessary or correlated screening tests
Telecommunications: phone-call fraud
Phone call model: destination of the call, duration, time of day or
week. Analyze patterns that deviate from an expected norm
Retail industry
Analysts estimate that 38% of retail shrink is due to dishonest
employees
Anti-terrorism

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Knowledge Discovery (KDD) Process

Data miningcore Pattern Evaluation

of knowledge
discovery process
Data Mining

Task-relevant Data

Data Warehouse Selection

Data Cleaning

Data Integration

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Databases
 KDD Process: Several Key Steps
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.
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Use of discovered knowledge
Data Mining and Business Intelligence

Increasing potential
to support
business decisions End User
Decision
Making

Data Presentation Business

Analyst
Visualization Techniques
Data Mining Data
Information Discovery Analyst

Data Exploration
Statistical Summary, Querying, and Reporting

Data Preprocessing/Integration, Data Warehouses

DBA
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems 12
Data Mining: Confluence of Multiple
Disciplines

Database
Technology Statistics

Machine Visualization
Learning Data Mining

Pattern
Recognition Other
Algorithm Disciplines

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Why Not Traditional Data Analysis?
Tremendous amount of data
Algorithms must be highly scalable to handle such as tera-bytes
of data
High-dimensionality of data
Micro-array may have tens of thousands of dimensions
High complexity of data
Data streams and sensor data
Time-series data, temporal data, sequence data
Structure data, graphs, social networks and multi-linked data
Heterogeneous databases and legacy databases
Spatial, spatiotemporal, multimedia, text and Web data
Software programs, scientific simulations
New and sophisticated applications

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Multi-Dimensional View of Data
Mining
Data to be mined
Relational, data warehouse, transactional, stream, object-
oriented/relational, active, spatial, time-series, text, multi-media,
heterogeneous, legacy, WWW
Knowledge to be mined
Characterization, discrimination, association, classification, clustering,
trend/deviation, outlier analysis, etc.
Multiple/integrated functions and mining at multiple levels
Techniques utilized
Database-oriented, data warehouse (OLAP), machine learning,
statistics, visualization, etc.
Applications adapted
Retail, telecommunication, banking, fraud analysis, bio-data mining,
stock market analysis, text mining, Web mining, etc.

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Data Mining: Classification Schemes

General functionality
Descriptive data mining
Predictive data mining
Different views lead to different classifications
Data view: Kinds of data to be mined
Knowledge view: Kinds of knowledge to be
discovered
Method view: Kinds of techniques utilized
Application view: Kinds of applications adapted

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Data Mining: On What Kinds of
Data?

Database-oriented data sets and applications

Relational database, data warehouse, transactional database
Advanced data sets and advanced applications
Data streams and sensor data
Time-series data, temporal data, sequence data (incl. bio-sequences)
Structure data, graphs, social networks and multi-linked data
Object-relational databases
Heterogeneous databases and legacy databases
Spatial data and spatiotemporal data
Multimedia database
Text databases
The World-Wide Web

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Data Mining Functionalities

Multidimensional concept description: Characterization and

discrimination
Generalize, summarize, and contrast data characteristics, e.g.,
dry vs. wet regions
Frequent patterns, association, correlation vs. causality
Diaper Beer [0.5%, 75%] (Correlation or causality?)
Classification and prediction
Construct 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)
Predict some unknown or missing numerical values

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 Data Mining Functionalities (2)
Cluster analysis
Class label is unknown: Group data to form new classes, e.g.,
cluster houses to find distribution patterns
Maximizing intra-class similarity & minimizing interclass
similarity
Outlier analysis
Outlier: Data object that does not comply with the general
behavior of the data
Noise or exception? Useful in fraud detection, rare events
analysis
Trend and evolution analysis
Trend and deviation: e.g., regression analysis
Periodicity analysis
Similarity-based analysis
Other pattern-directed or statistical analyses

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Are All the Discovered Patterns
Interesting?

Data mining may generate thousands of patterns: Not all of them are
interesting
Suggested approach: Human-centered, query-based, focused mining
Interestingness measures
A pattern is interesting if it is easily understood by humans, valid on
new or test data with some degree of certainty, potentially useful,
novel, or validates some hypothesis that a user seeks to confirm
Objective vs. subjective interestingness measures
Objective: based on statistics and structures of patterns, e.g.,
support, confidence, etc.
Subjective: based on users belief in the data, e.g., unexpectedness,
novelty, actionability, etc.

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Find All and Only Interesting Patterns?

Find all the interesting patterns: Completeness

Can a data mining system find all the interesting patterns? Do
we need to find all of the interesting patterns?
Heuristic vs. exhaustive search
Association vs. classification vs. clustering
Search for only interesting patterns: An optimization problem
Can a data mining system find only the interesting patterns?
Approaches
First general all the patterns and then filter out the
uninteresting ones
Generate only the interesting patternsmining query
optimization
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Why Data Mining Query Language?

Automated vs. query-driven?

Finding all the patterns autonomously in a database?unrealistic
because the patterns could be too many but uninteresting
Data mining should be an interactive process
User directs what to be mined
Users must be provided with a set of primitives to be used to communicate
with the data mining system
Incorporating these primitives in a data mining query language
More flexible user interaction
Foundation for design of graphical user interface
Standardization of data mining industry and practice

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DMQLA Data Mining Query Language

Motivation
A DMQL can provide the ability to support ad-hoc and
interactive data mining
By providing a standardized language like SQL
Hope to achieve a similar effect like that SQL has on
relational database
Foundation for system development and evolution
Facilitate information exchange, technology
transfer, commercialization and wide acceptance

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An Example Query in DMQL

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Integration of Data Mining and Data
Warehousing

Data mining systems, DBMS, Data warehouse systems coupling

No coupling, loose-coupling, semi-tight-coupling, tight-coupling

On-line analytical mining data

integration of mining and OLAP technologies

Interactive mining multi-level knowledge

Necessity of mining knowledge and patterns at different levels of

abstraction by drilling/rolling, pivoting, slicing/dicing, etc.

Integration of multiple mining functions

Characterized classification, first clustering and then association

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Coupling Data Mining with DB/DW Systems

No couplingflat file processing, not recommended

Loose coupling
Fetching data from DB/DW
Semi-tight couplingenhanced DM performance
Provide efficient implement a few data mining primitives in a
DB/DW system, e.g., sorting, indexing, aggregation,
histogram analysis, multiway join, precomputation of some
stat functions
Tight couplingA uniform information processing environment
DM is smoothly integrated into a DB/DW system, mining
query is optimized based on mining query, indexing, query
processing methods, etc.

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Architecture: Typical Data Mining System

Graphical User Interface

Pattern Evaluation
Knowl
Data Mining Engine edge-
Base
Database or Data
Warehouse Server

data cleaning, integration, and selection

Data World-Wide Other Info

Database Repositories
Warehouse Web
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 Major Issues in Data Mining
Mining methodology
Mining different kinds of knowledge from diverse data types, e.g., bio,
stream, Web
Performance: efficiency, effectiveness, and scalability
Pattern evaluation: the interestingness problem
Incorporation of background knowledge
Handling noise and incomplete data
Parallel, distributed and incremental mining methods
Integration of the discovered knowledge with existing one: knowledge
fusion
User interaction
Data mining query languages and ad-hoc mining
Expression and visualization of data mining results
Interactive mining of knowledge at multiple levels of abstraction
Applications and social impacts
Domain-specific data mining & invisible data mining
Protection of data security, integrity, and privacy 28
What Is Association Rule Mining?

Frequent patterns: patterns (set of items,

sequence, etc.) that occur frequently in a
database [AIS93]
Frequent pattern mining: finding
regularities in data
What products were often purchased together?
Beer and diapers?!
What are the subsequent purchases after buying
a car?
Can we automatically profile customers?

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Basics

Itemset: a set of
items Transaction database TDB
E.g., acm={a, c, m}
Support of itemsets TID Items bought
Sup(acm)=3 100 f, a, c, d, g, I, m, p
Given min_sup=3, 200 a, b, c, f, l,m, o
acm is a frequent 300 b, f, h, j, o
pattern 400 b, c, k, s, p
Frequent pattern 500 a, f, c, e, l, p, m, n
mining: find all
frequent patterns in a
database
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Frequent Pattern Mining Methods

Apriori and its variations/improvements

Mining frequent-patterns without
candidate generation
Mining max-patterns and closed
itemsets
Mining multi-dimensional, multi-level
frequent patterns with flexible support
constraints
Interestingness: correlation and
causality
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Apriori: Candidate Generation-and-test

Any subset of a frequent itemset must be

also frequent an anti-monotone property
A transaction containing {beer, diaper, nuts}
also contains {beer, diaper}
{beer, diaper, nuts} is frequent {beer,
diaper} must also be frequent
No superset of any infrequent itemset
should be generated or tested
Many item combinations can be pruned

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Apriori-based Mining

Generate length (k+1) candidate

itemsets from length k frequent
itemsets, and
Test the candidates against DB

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 Apriori Algorithm

A level-wise, candidate-generation-and-test approach

(Agrawal & Srikant 1994)
Data base D 1-candidates Freq 1-itemsets 2-candidates
TID Items Itemset Sup Itemset Sup Itemset
10 a, c, d a 2 a 2 ab
20 b, c, e Scan D b 3 b 3 ac
30 a, b, c, e c 3 c 3 ae
40 b, e d 1 e 3 bc
e 3 be
Min_sup=2 ce
3-candidates Freq 2-itemsets Counting
Scan D Itemset Itemset Sup Itemset Sup
bce ac 2 ab 1
bc 2 ac 2 Scan D
be 3 ae 1
Freq 3-itemsets ce 2 bc 2
Itemset Sup be 3
bce 2 ce 2
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The Apriori Algorithm

Ck: Candidate itemset of size k

Lk : frequent itemset of size k

L1 = {frequent items};
for (k = 1; Lk !=; k++) do
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
return k Lk;

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Important Details of Apriori

How to generate candidates?

Step 1: self-joining Lk
Step 2: pruning
How to count supports of candidates?

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How to Generate Candidates?

Suppose the items in Lk-1 are listed in an

order
Step 1: self-join 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: pruning
For each itemset c in Ck do
For each (k-1)-subsets s of c do if (s is not in
Lk-1) then delete c from Ck

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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 Count Supports of Candidates?

Why counting supports of candidates a

problem?
The total number of candidates can be very
huge
One transaction may contain many candidates
Method:
Candidate itemsets are stored in a hash-tree
Leaf node of hash-tree contains a list of itemsets
and counts
Interior node contains a hash table
Subset function: finds all the candidates
contained in a transaction
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Challenges of Frequent Pattern Mining

Challenges
Multiple scans of transaction database
Huge number of candidates
Tedious workload of support counting for
candidates
Improving Apriori: general ideas
Reduce number of transaction database scans
Shrink number of candidates
Facilitate support counting of candidates

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 Summary

Data mining: Discovering interesting patterns from large amounts

of data
A natural evolution of database technology, in great demand, with
wide applications
A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
Mining can be performed in a variety of information repositories
Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis,
etc.
Data mining systems and architectures
Major issues in data mining
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 A Brief History of Data Mining Society

1989 IJCAI Workshop on Knowledge Discovery in Databases

Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W.
Frawley, 1991)
1991-1994 Workshops on Knowledge Discovery in Databases
Advances in Knowledge Discovery and Data Mining (U. Fayyad, G.
Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)
1995-1998 International Conferences on Knowledge Discovery in Databases
and Data Mining (KDD95-98)
Journal of Data Mining and Knowledge Discovery (1997)
ACM SIGKDD conferences since 1998 and SIGKDD Explorations
More conferences on data mining
PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM
(2001), etc.
ACM Transactions on KDD starting in 2007
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Conferences and Journals on Data Mining

KDD Conferences Other related conferences

ACM SIGKDD Int. Conf. on ACM SIGMOD
Knowledge Discovery in VLDB
Databases and Data Mining (IEEE) ICDE
(KDD)
WWW, SIGIR
SIAM Data Mining Conf. (SDM)
(IEEE) Int. Conf. on Data Mining ICML, CVPR, NIPS
(ICDM) Journals
Conf. on Principles and Data Mining and Knowledge
practices of Knowledge Discovery (DAMI or DMKD)
Discovery and Data Mining IEEE Trans. On Knowledge and
(PKDD)
Data Eng. (TKDE)
Pacific-Asia Conf. on Knowledge
Discovery and Data Mining KDD Explorations
(PAKDD) ACM Trans. on KDD

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Where to Find References? DBLP, CiteSeer,
Google
Data mining and KDD (SIGKDD: CDROM)
Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD
Database systems (SIGMOD: ACM SIGMOD AnthologyCD ROM)
Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT,
DASFAA
Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.
AI & Machine Learning
Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory),
CVPR, NIPS, etc.
Journals: Machine Learning, Artificial Intelligence, Knowledge and Information
Systems, IEEE-PAMI, etc.
Web and IR
Conferences: SIGIR, WWW, CIKM, etc.
Journals: WWW: Internet and Web Information Systems,
Statistics
Conferences: Joint Stat. Meeting, etc.
Journals: Annals of statistics, etc.
Visualization
Conference proceedings: CHI, ACM-SIGGraph, etc.
Journals: IEEE Trans. visualization and computer graphics, etc. 44
 Recommended Reference Books
S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan
Kaufmann, 2002
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000
T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery
and Data Mining. AAAI/MIT Press, 1996
U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge
Discovery, Morgan Kaufmann, 2001
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed.,
2006
D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Springer-Verlag, 2001
T. M. Mitchell, Machine Learning, McGraw Hill, 1997
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991
P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005
S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques
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with Java Implementations, Morgan Kaufmann, 2nd ed. 2005