An Introduction to Data Mining Padhraic Smyth Information and Computer Science University of...

An Introduction to Data Mining

Padhraic SmythInformation and Computer Science

University of California, Irvine

July 2000

Today’s talk:

An introduction to data mining

General concepts

Focus on current practice of data mining: mainmessage is be aware of the “hype factor”

Wednesday’s talk:

Application of ideas in data mining to problems inatmospheric/environmental science

Outline of Today’s Talk

• What is Data Mining?

• Computer Science and Statistics: a Brief History

• Models and Algorithms

• Hot Topics in Data Mining

• Conclusions

The Data Revolution

• Context – “.. drowning in data, but starving for knowledge” – Ubiquitous in business, science, medicine, military– Analyzing/exploring data manually becomes difficult with massive data sets

• Viewpoint: data as a resource– Data themselves are not of direct use– How can we leverage data to make better decisions ?

Technology is a Driving Factor

• Larger, cheaper memory– Moore’s law for magnetic disk density

“capacity doubles every 18 months” (Jim Gray, Microsoft)– storage cost per byte falling rapidly

• Faster, cheaper processors– can analyze more data– fit more complex models– invoke massive search techniques– more powerful visualization

Massive Data Sets

• Characteristics– very large N (billions)– very large d (thousands or millions)– heterogeneous– dynamic– (Note: in scientific applications there is often a temporal and/or

spatial dimension)

1 2 . . . . . . . . . . . d12....N

High-dimensional data

• Volume of sphere relative to cube in d dimensions?

Hypersphere in d dimensions

Hypercubein d dimensions

Rel. Volume 0.79 ? ? ? ? ?

Dimension 2 3 4 5 6 7

(David Scott, Multivariate Density Estimation, Wiley, 1992)

High-dimensional data

Hypersphere in d dimensions

Hypercubein d dimensions

Rel. Volume 0.79 0.53 0.31 0.16 0.08 0.04

Dimension 2 3 4 5 6 7

• high-d, uniform => most data points will be “out” at the corners

• high-d space is sparse: and non-intuitive

What is data mining?


“Data-driven discovery of models and patterns from massive observational data sets”


“The magic phrase to put in every funding proposalyou write to NSF, DARPA, NASA, etc”


“The magic phrase you use to sell your….. - database software - statistical analysis software - parallel computing hardware - consulting services”


“Data-driven discovery of models and patterns from

massive observational data sets”

Statistics,Inference




LanguagesandRepresentations




Engineering,Data ManagementLanguages,

Representations




Engineering,Data Management

Languages,Representations

Applications

Who is involved in Data Mining?

• Business Applications– customer-based, transaction-oriented applications– very specific applications in fraud, marketing, credit-scoring

• in-house applications (e.g., AT&T, Microsoft, etc)• consulting firms: considerable hype factor!

– largely involve the application of existing statistical ideas, scaled up to massive data sets (“engineering”)

• Academic Researchers– mainly in computer science – extensions of existing ideas, significant “bandwagon effect”– largely focused on prediction with multivariate data

• Bottom Line: – primarily computer scientists, often with little knowledge of statistics, main focus is on

algorithms

Myths and Legends in Data Mining

• “Data analysis can be fully automated”

– human judgement is critical in almost all applications

– “semi-automation” is however very useful





• “Association rules are useful”

– association rules are essentially lists of correlations

– no documented successful application

– compare with decision trees (numerous applications)





• “Association rules are useful”

– association rules are essentially lists of correlations

– no documented successful application

– compare with decision trees (numerous applications)

• “With massive data sets you don’t need statistics”

– massiveness brings heterogeneity - even more statistics

Current Data Mining Software

1. General purpose tools

– software systems for data mining (IBM, SGI, etc)

• just simple statistical algorithms with SQL?

• limited support for temporal, spatial data

– some successes (difficult to validate)

• banking, marketing, retail

• mainly useful for large-scale EDA?

– “mining the miners” (Jerry Friedman):

• similar to expert systems/neural networks hype in 80’s?

Transaction Data and Association Rules

• Supermarket example: (Srikant and Agrawal, 1997)

– #items = 500,000, #transactions = 1.5 million

ItemsTransa

ctions x x

xx

x x xx

x x xxx x

xx

x

xx

x

Transaction Data and Association Rules

• Example of an Association Rule If a customer buys beer they will also buy chips

– p(chips|beer) = “confidence”

– p(beer) = “support”

ItemsTransa

ctions x x

xx

x x xx

x x xxx x

xx

x

xx

x

Current Data Mining Software

2. Special purpose (“niche”) applications

- fraud detection, direct-mail marketing, credit-scoring,etc.

- often solve high-dimensional classification/regression problems

- Telephone industry applications

- fraud

- Direct-mail advertising

- find new customers

- increase # home-equity loans

- common theme: “track the customer!”

- difficult to validate claims of success (few publications)

Advanced Scout

• Background– every NBA game is annotated (each pass, shot, foul, etc.)– potential competitive advantage for coaches– Problem: over a season, this generates alot of data!

• Solution (Bhandari et al, IBM, 1997)– “attribute focusing” finds conditional ranges on attributes where the distributions

differ from the norm– generates descriptions of interesting patterns

e.g., “Player X made 100% of his shots when when Player Y was in the game: X normally makes only 50% of his shots”

• Status– used by 28 of the 29 teams in the NBA– an intelligent assistant

AT&T Classification of Telephone Numbers

• Background

– AT&T has about 100 million customers

– It logs 300 million calls per day, 40 attributes each

– 350 million unique telephone numbers

– Which are business and which are residential?

• Solution (Pregibon and Cortes, AT&T,1997)

– Proprietary model, using a few attributes, trained on known business customers to adaptively track p(business|data)

– Significant systems engineering: data are downloaded nightly, model updated (20 processors, 6Gb RAM, terabyte disk farm)

• Status:

– invaluable evolving “snapshot” of phone usage in US for AT&T

– basis for fraud detection, marketing, and other applications

Bad Debt Prediction

• Background– Bank has 120,000 accounts which are delinquent– employs 500 collectors– process is expensive and inefficient

• Predictive Modeling– target variable: amount repaid within 6 months– input variables: 2000 different variables derived from credit history– model outputs are used to “score” each debtor based on likelihood of paying

• Results– decision trees, “bump-hunting” used to score customers

• non-trivial software issues in handling such large data sets– “scoring” system in routine use– estimated savings to bank are in millions/annum

Outline



Historical Context: Statistics

• Gauss, Fisher, and all that– least-squares, maximum likelihood– development of fundamental principles

• The Mathematical Era– 1950’s: Neyman, etc: the mathematicians take over

• The Computational Era– steadily growing since the 1960’s

• note: “data mining/fishing” viewed very negatively!– 1970’s: EDA, Bayesian estimation, flexible models, EM, etc– a growing awarness of the power and role of computing in data

analysis

Historical Context: Computer Science

• Pattern Recognition and AI

– focus on perceptual problems (e.g., speech, images)

– 1960’s: bifurcation into statistical and non-statistical approaches, e.g., grammars

– convergence of applied statistics and engineering

• e.g., statistical image analysis: Geman, Grenander, etc

• Machine Learning and Neural Networks

– 1980’s: failure of non-statistical learning approaches

– emergence of flexible models (trees, networks)

– convergence of applied statistics and learning

• e.g., work of Friedman, Spiegelhalter, Jordan, Hinton

The Emergence of Data Mining

• Distinct threads of evolution

– AI/machine learning

• 1989 KDD workshop -> ACM SIGKDD 2000

• focus on “automated discovery, novelty”

– Database Research

• focus on massive data sets

• e.g., SIGMOD -> association rules, scalable algorithms

– “Data Owners”

• what can we do with all this data in our RDBMS?

• primarily customer-oriented transaction data owners

• industry dominated, applications-oriented

The Emergence of Data Mining

• The “Mother in Law” phenomenon

• even your mother-in-law has heard about data mining

• Beware of the hype!

– remember expert systems, neural nets, etc

– basically sound ideas that were oversold creating a backlash

Computer ScienceStatistics

Statistics Computer Science

StatisticalPatternRecognition

Neural Networks

MachineLearning

DataMining

DatabasesStatisticalInference



Neural Networks

MachineLearning

DataMining


Where Work is Published

JASA,JRSS

IEEE PAMIICPRICCV

NIPSNeural Comp.

ICMLCOLTML Journal

KDDIJDMKD

SIGMODVLDB



Neural Networks

MachineLearning

DataMining


NonlinearRegression

PatternFindingComputer Vision,

Signal Recognition

FlexibleClassificationModels

ScalableAlgorithmsGraphical

ModelsHiddenVariableModels

Focus Areas

NonlinearRegression


Signal Recognition


ScalableAlgorithms

GraphicalModels

HiddenVariableModels

More Statistical More Algorithmic

General Characteristics

NonlinearRegression


Signal Recognition


ScalableAlgorithms

GraphicalModels



Continuous Signals Categorical Data


NonlinearRegression


Signal Recognition


ScalableAlgorithms

GraphicalModels





Model-Based “Model-free”

NonlinearRegression


Signal Recognition


ScalableAlgorithms

GraphicalModels




Time/Space Modeling Multivariate Data


Model-Based “Model-free”

NonlinearRegression


Signal Recognition


ScalableAlgorithms

GraphicalModels


“Hot Topics”

HiddenMarkov Models

BeliefNetworks

SupportVectorMachines

Mixture/Factor Models

Classification Trees

AssociationRules

DeformableTemplates

ModelCombining

Implications

• The “renaissance data miner” is skilled in:– statistics: theories and principles of inference– modeling: languages and representations for data– optimization and search– algorithm design and data management

• The educational problem– is it necessary to know all these areas in depth?– Is it possible?– Do we need a new breed of professionals?

• The applications viewpoint:– How does a scientist or business person keep up with all these developments? – How can they choose the best approach for their problem

Outline




E.g., multivariate,

continuous/categorical,

temporal, spatial,

combinations, etc

Data Set

TaskE.g., Exploration,

Prediction, Clustering,

Density Estimation,

Pattern Discovery

Data Set

TaskData Set

Model

Language/Representation:

Underlying functional form

used for representation, e.g.,

linear functions, hierarchies,

rules/boxes, grammars, etc

TaskData Set

Model

Score Function

Statistical Inference:

How well a model fits data, e.g.,

square-error, likelihood,

classification loss, query

match, interpretation

TaskData Set

Model

Score Function

Optimization Computational method

used to optimize score function,

given the model and score

function, e.g., hill-climbing,

greedy search, linear programming

Modeling

TaskData Set

Model

Score Function

Optimization

Data Access

Actual instantiation as an algorithm

with data structures, efficient

implementation, etc.

Modeling

Algorithm

TaskData Set

Model

Score Function

Optimization

Data Access

Human Evaluation/Decisions

Modeling

Algorithm

PredictionMultivariate

Hierarchical representation of

piecewise constant mapping

Cross-Validation

Greedy Search

Flat File

Accuracy and Interpretability

CART

Emphasis on

predictive power

and flexibility

of model

ExploratoryTransaction

Sets of local rules/

conditional probabilities

Thresholds on p

Systematic Search

Relational Database

????

Association Rules

Emphasis on

computational

efficiency and

data access

The Reductionist Viewpoint

• Methodology

– reduce problems to fundamental components

– think in terms of components first, algorithms second

– ultimately the application should “drive” the algorithm

– allows systematic comparison and synthesis

– clarifies relative role of statistics, databases, search, etc

Cultural Differences

• Computer Scientists:

– often have little exposure to the “modeling art” of data analysis

– tend to stick to a small set of well-understood models and problems

– papers focus on algorithms, not models

– but are typically good at making things run fast

• Statisticians:

– applied statisticians are often very good at the “art” component

– little experience with the data management/engineering part

– papers focus on models, not algorithms

• Bottom line

– the computer scientists get more attention since they are much savvier at marketing new ideas than the statisticians

– The “right” way: systematically combine both statistics and engineering/CS, beware of hype

Outline




• Hot Topics in Data Mining

Hot Topics

• 1. Flexible Prediction Models

• 2. Scalable Algorithms

• 3. Pattern Discovery

• 4. Graphical Models

• 5. Hidden Variable Models

• 6. Deformable Templates

• 7. Heterogenous Data

Today’s talk

Wednesday’s talk

1. Flexible Prediction Models

• Model Combining:

– Stacking

• linear combinations of models with X-validated weights

– Bagging

• equally weighted combinations trained on bootstrap samples

– Boosting

• iterative re-training on data points which contribute to error

• Flexible Model Forms

– Decision trees

– Neural networks

– Support vector machines

2. Scalable Algorithms

• How far away are the data?

Memory

RAM

Disk



Memory RandomAccess Time

RAM 10-8 seconds

Disk 10-3 seconds



Memory Random EffectiveAccess Time Distance

RAM 10-8 seconds 1 meter

Disk 10-3 seconds 100 km


• “Scaling down the data” or “data approximation”– work from clever data summarizations (e.g., sufficient statistics)

• Squashing (DuMouchel et al, AT&T, KDD ‘99) – create a small “pseudo data set” – similar statistical properties to the original (massive) data set – now run your standard algorithm on the pseudo-data– can be significantly better than random sampling– interesting theoretical (statistical) basis

• Frequent Itemsets– find all tuples which with more than T occurrences in D– (basis for association rule algorithms)– itemsets: cheap computational way to generate joint probabilities– use maximum entropy to construct full model from itemsets (Pavlov, Mannila, and

Smyth, KDD 99)


• “Scaling up the algorithm”

– data structures and caching strategies to speed up known algorithms

– typically orders of magnitude speed improvements

• Exact Algorithms

– BOAT (Gehrke et al, SIGMOD 98):

• a scalable decision tree construction algorithm

• clever algorithms can work from only 2 scans

– ADTrees (Moore, CMU, 1998)

• clever data structures for caching sufficient statistics for multivariate categorical data

• Approximate Algorithms

– approximate EM for Gaussian mixture modeling (Bradley and Fayyad, KDD 98)

– various heuristics for caching, approximation

3. Pattern Finding

• Patterns = unusual hard-to-find local “pockets” of data

– finding patterns is not the same as global model fitting

– the simplest example of patterns are association rules

• “Bump-hunting”

– PRIM algorithm of Friedman and Fisher (1999)

– finds multivariate “boxes” in high-dimensional spaces where mean of target variable is higher

– effective and flexible

• e.g., finding small highly profitable groups of customers

“Bump-Hunting”

Pattern Finding in Sequence Data

• Clustering Sequences– sequences of different lengths from different individuals

• e.g. sequences of Web-page requests– Problem: do the sequences cluster into groups?– Clustering problem is non-trivial:

• distance between 2 sequences of different lengths?

• Model-based approach (Cadez, Heckerman, Smyth, KDD 2000)– each cluster described as a Markov model– defines a mixture of Markov models, EM used for clustering– Application to MSNBC.com Web data

• 900,000 users/sequences per day• clustered into order of 100 groups• useful for visualization/exploration of massive Web log

Clusters of Dynamic Behavior

B

C

D

A

B

C

D

A

B

C

D

A

Cluster 1 Cluster 2

Cluster 3

Final Comments

• Successful data mining requires integration of – statistics– computer science– the application discipline

• Current practice of data mining– computer scientists focused on business applications– relatively little statistical sophistication: but some new ideas– considerable “hype” factor

• Wednesday’s talk:– new ideas in temporal and spatial models– new ideas in latent variable modeling – potential applications in atmospheric/environmental science

Further Reading

• Papers:– www.ics.uci.edu/~datalab– e.g., see P. Smyth, “Data mining: data analysis on a grand scale?”,

preprint of review paper to appear in Statistical Methods in Medical Research

• Text (forthcoming)

– Principles of Data Mining

• D. J Hand, H. Mannila, P. Smyth

• MIT Press, late 2000

3. Pattern Finding

• Contrast Sets (Bay and Pazzani, KDD99)

– individuals or objects categorized into 2 groups

• e.g., students enrolled in CS and in Engineering

– high-dimensional multivariate measurements on each

– Problem: automatically summarize the significant differences between the two groups.

• e.g., [fraction of ESL >] AND [mean SAT >] in CS

• Approach

– massive systematic breadth-first search through potential variable-value conjunctions

– branch-and-bound pruning of exponentially large search space

– statistical adjustments for multiple hypothesis problem

3. Pattern Finding

• Contrast Sets (Bay and Pazzani, KDD99)– individuals or objects categorized into 2 groups

• e.g., students enrolled in CS and in Engineering– high-dimensional multivariate measurements on each – automatically produces a summary of significant differences between

groups (Bay and Pazzani, KDD ‘99)– combines massive search with statistical estimation

• Time-Series Pattern Spotting– “find me a shape that looks like this”– semi-Markov deformable templates (Ge and Smyth, KDD 2000)– significantly outperforms template matching and DTW– Bayesian approach integrates prior knowledge with data

Example: Deformable Templates

Each waveform segment corresponds to a state in the model. Segmental hidden semi-Markov model

S1 S2ST

- - - - - - - -

Segments

States

Pattern-Based End-Point Detection

0 50 100 150 200 250 300 350 400200

300

400

500

0 50 100 150 200 250 300 350 400200

300

400

500

TIME (SECONDS)

Original Pattern

Detected Pattern

End-Point Detection in Semiconductor Manufacturing

Heterogeneous Data Modeling

• Clustering Objects (sequences, curves, etc)

– probabilistic approach: define a mixture of models (Cadez, Gaffney, and Smyth, KDD 2000)

– unified framework for clustering objects of different dimensions

– applications:

• curve-clustering:

– e.g., mixture of regression models (Gaffney and Smyth (KDD ‘99)

– video movement, gene expression data, storm trajectories

• sequence clustering

– e.g., mixtures of Markov models

– clustering of MSNBC Web data (Cadez et al, KDD ‘00)

0 5 10 15 20 25 3040

60

80

100

120

140

160

TIME

X-P

OS

ITIO

N

TRAJECTORIES OF CENTROIDS OF MOVING HAND IN VIDEO STREAMS

0 5 10 15 20 25 300

10

20

30

40

50

60

70

80

TIME

Y-P

OS

ITIO

N

ESTIMATED CLUSTER TRAJECTORY

0 5 10 15 20 25 3085

90

95

100

105

110

115

120

125

TIME

X-P

OS

ITIO

N

ESTIMATED CLUSTER TRAJECTORY

Heterogenous Populations of Objects

Population Model

in parameter space

Individuals

and Parameters

Observed Data

An Introduction to Data Mining Padhraic Smyth Information and Computer Science University of...

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