Post on 21-Dec-2015
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?
What is data mining?
“Data-driven discovery of models and patterns from massive observational data sets”
What is data mining?
“The magic phrase to put in every funding proposalyou write to NSF, DARPA, NASA, etc”
What is data mining?
“The magic phrase you use to sell your….. - database software - statistical analysis software - parallel computing hardware - consulting services”
What is data mining?
“Data-driven discovery of models and patterns from
massive observational data sets”
Statistics,Inference
What is data mining?
“Data-driven discovery of models and patterns from massive observational data sets”
Statistics,Inference
LanguagesandRepresentations
What is data mining?
“Data-driven discovery of models and patterns from massive observational data sets”
Statistics,Inference
Engineering,Data ManagementLanguages,
Representations
What is data mining?
“Data-driven discovery of models and patterns from massive observational data sets”
Statistics,Inference
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
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)
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)
• “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
• What is Data Mining?
• Computer Science and Statistics: a Brief History
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
Statistics Computer Science
StatisticalPatternRecognition
Neural Networks
MachineLearning
DataMining
DatabasesStatisticalInference
Where Work is Published
JASA,JRSS
IEEE PAMIICPRICCV
NIPSNeural Comp.
ICMLCOLTML Journal
KDDIJDMKD
SIGMODVLDB
Statistics Computer Science
StatisticalPatternRecognition
Neural Networks
MachineLearning
DataMining
DatabasesStatisticalInference
NonlinearRegression
PatternFindingComputer Vision,
Signal Recognition
FlexibleClassificationModels
ScalableAlgorithmsGraphical
ModelsHiddenVariableModels
Focus Areas
NonlinearRegression
PatternFindingComputer Vision,
Signal Recognition
FlexibleClassificationModels
ScalableAlgorithms
GraphicalModels
HiddenVariableModels
More Statistical More Algorithmic
General Characteristics
NonlinearRegression
PatternFindingComputer Vision,
Signal Recognition
FlexibleClassificationModels
ScalableAlgorithms
GraphicalModels
HiddenVariableModels
More Statistical More Algorithmic
Continuous Signals Categorical Data
General Characteristics
NonlinearRegression
PatternFindingComputer Vision,
Signal Recognition
FlexibleClassificationModels
ScalableAlgorithms
GraphicalModels
HiddenVariableModels
More Statistical More Algorithmic
Continuous Signals Categorical Data
General Characteristics
Model-Based “Model-free”
NonlinearRegression
PatternFindingComputer Vision,
Signal Recognition
FlexibleClassificationModels
ScalableAlgorithms
GraphicalModels
HiddenVariableModels
More Statistical More Algorithmic
Continuous Signals Categorical Data
Time/Space Modeling Multivariate Data
General Characteristics
Model-Based “Model-free”
NonlinearRegression
PatternFindingComputer Vision,
Signal Recognition
FlexibleClassificationModels
ScalableAlgorithms
GraphicalModels
HiddenVariableModels
“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
• What is Data Mining?
• Computer Science and Statistics: a Brief History
• Models and Algorithms
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
• What is Data Mining?
• Computer Science and Statistics: a Brief History
• Models and Algorithms
• 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
2. Scalable Algorithms
• How far away are the data?
Memory RandomAccess Time
RAM 10-8 seconds
Disk 10-3 seconds
2. Scalable Algorithms
• How far away are the data?
Memory Random EffectiveAccess Time Distance
RAM 10-8 seconds 1 meter
Disk 10-3 seconds 100 km
2. Scalable Algorithms
• “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)
2. Scalable Algorithms
• “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”
“Bump-Hunting”
“Bump-Hunting”
“Bump-Hunting”
“Bump-Hunting”
“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