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Data Mining in Market Research

- What is data mining?
- Methods for finding interesting structure in

large databases - E.g. patterns, prediction rules, unusual cases
- Focus on efficient, scalable algorithms
- Contrasts with emphasis on correct inference in

statistics - Related to data warehousing, machine learning
- Why is data mining important?
- Well marketed now a large industry pays well
- Handles large databases directly
- Can make data analysis more accessible to end

users - Semi-automation of analysis
- Results can be easier to interpret than e.g.

regression models - Strong focus on decisions and their implementation

CRISP-DM Process Model

Data Mining Software

- Many providers of data mining software
- SAS Enterprise Miner, SPSS Clementine, Statistica

Data Miner, MS SQL Server, Polyanalyst,

KnowledgeSTUDIO, - See http//www.kdnuggets.com/software/suites.html

for a list - Good algorithms important, but also need good

facilities for handling data and meta-data - Well use
- WEKA (Waikato Environment for Knowledge Analysis)
- Free (GPLed) Java package with GUI
- Online at www.cs.waikato.ac.nz/ml/weka
- Witten and Frank, 2000. Data Mining Practical

Machine Learning Tools and Techniques with Java

Implementations. - R packages
- E.g. rpart, class, tree, nnet, cclust, deal,

GeneSOM, knnTree, mlbench, randomForest, subselect

Data Mining Terms

- Different names for familiar statistical

concepts, from database and AI communities - Observation case, record, instance
- Variable field, attribute
- Analysis of dependence vs interdependence

Supervised vs unsupervised learning - Relationship association, concept
- Dependent variable response, output
- Independent variable predictor, input

Common Data Mining Techniques

- Predictive modeling
- Classification
- Derive classification rules
- Decision trees
- Numeric prediction
- Regression trees, model trees
- Association rules
- Meta-learning methods
- Cross-validation, bagging, boosting
- Other data mining methods include
- artificial neural networks, genetic algorithms,

density estimation, clustering, abstraction,

discretisation, visualisation, detecting changes

in data or models

Classification

- Methods for predicting a discrete response
- One kind of supervised learning
- Note in biological and other sciences,

classification has long had a different meaning,

referring to cluster analysis - Applications include
- Identifying good prospects for specific marketing

or sales efforts - Cross-selling, up-selling when to offer

products - Customers likely to be especially profitable
- Customers likely to defect
- Identifying poor credit risks
- Diagnosing customer problems

Weather/Game-Playing Data

- Small dataset
- 14 instances
- 5 attributes
- Outlook - nominal
- Temperature - numeric
- Humidity - numeric
- Wind - nominal
- Play
- Whether or not a certain game would be played
- This is what we want to understand and predict

ARFF file for the weather data.

German Credit Risk Dataset

- 1000 instances (people), 21 attributes
- class attribute describes people as good or bad

credit risks - Other attributes include financial information

and demographics - E.g. checking_status, duration, credit_history,

purpose, credit_amount, savings_status,

employment, Age, housing, job, num_dependents,

own_telephone, foreign_worker - Want to predict credit risk
- Data available at UCI machine learning data

repository - http//www.ics.uci.edu/mlearn/MLRepository.html
- and on 747 web page
- http//www.stat.auckland.ac.nz/reilly/credit-g.ar

ff

Classification Algorithms

- Many methods available in WEKA
- 0R, 1R, NaiveBayes, DecisionTable, ID3, PRISM,

Instance-based learner (IB1, IBk), C4.5 (J48),

PART, Support vector machine (SMO) - Usually train on part of the data, test on the

rest - Simple method Zero-rule, or 0R
- Predict the most common category
- Class ZeroR in WEKA
- Too simple for practical use, but a useful

baseline for evaluating performance of more

complex methods

1-Rule (1R) Algorithm

- Based on single predictor
- Predict mode within each value of that predictor
- Look at error rate for each predictor on training

dataset, and choose best predictor - Called OneR in WEKA
- Must group numerical predictor values for this

method - Common method is to split at each change in the

response - Collapse buckets until each contains at least 6

instances

1R Algorithm (continued)

- Biased towards predictors with more categories
- These can result in over-fitting to the training

data - But found to perform surprisingly well
- Study on 16 widely used datasets
- Holte (1993), Machine Learning 11, 63-91
- Often error rate only a few percentages points

higher than more sophisticated methods (e.g.

decision trees) - Produced rules that were much simpler and more

easily understood

Naïve Bayes Method

- Calculates probabilities of each response value,

assuming independence of attribute effects - Response value with highest probability is

predicted - Numeric attributes are assumed to follow a normal

distribution within each response value - Contribution to probability calculated from

normal density function - Instead can use kernel density estimate, or

simply discretise the numerical attributes

Naïve Bayes Calculations

- Observed counts and probabilities above
- Temperature and humidity have been discretised
- Consider new day
- Outlooksunny, temperaturecool, humidityhigh,

windytrue - Probability(playyes) a 2/9 x 3/9 x 3/9 x 3/9 x

9/14 0.0053 - Probability(playno) a 3/5 x 1/5 x 4/5 x 3/5 x

5/14 0.0206 - Probability(playno) 0.0206/(0.00530.0206)

79.5 - no four times more likely than yes

Naïve Bayes Method

- If any of the component probabilities are zero,

the whole probability is zero - Effectively a veto on that response value
- Add one to each cells count to get around this

problem - Corresponds to weak positive prior information
- Naïve Bayes effectively assumes that attributes

are equally important - Several highly correlated attributes could drown

out an important variable that would add new

information - However this method often works well in practice

Decision Trees

- Classification rules can be expressed in a tree

structure - Move from the top of the tree, down through

various nodes, to the leaves - At each node, a decision is made using a simple

test based on attribute values - The leaf you reach holds the appropriate

predicted value - Decision trees are appealing and easily used
- However they can be verbose
- Depending on the tests being used, they may

obscure rather than reveal the true pattern - More info online at http//recursive-partitioning.

com/

Decision tree with a replicated subtree

If x1 and y1 then class a If z1 and w1

then class a Otherwise class b

Problems with Univariate Splits

Constructing Decision Trees

- Develop tree recursively
- Start with all data in one root node
- Need to choose attribute that defines first split
- For now, we assume univariate splits are used
- For accurate predictions, want leaf nodes to be

as pure as possible - Choose the attribute that maximises the average

purity of the daughter nodes - The measure of purity used is the entropy of the

node - This is the amount of information needed to

specify the value of an instance in that node,

measured in bits

Tree stumps for the weather data

(a)

(b)

(c)

(d)

Weather Example

- First node from outlook split is for sunny,

with entropy 2/5 log2(2/5) 3/5 log2(3/5)

0.971 - Average entropy of nodes from outlook split is
- 5/14 x 0.971 4/14 x 0 5/14 x 0.971 0.693
- Entropy of root node is 0.940 bits
- Gain of 0.247 bits
- Other splits yield
- Gain(temperature)0.029 bits
- Gain(humidity)0.152 bits
- Gain(windy)0.048 bits
- So outlook is the best attribute to split on

Expanded tree stumps for weather data

(a)

(b)

(c)

Decision tree for the weather data

Decision Tree Algorithms

- The algorithm described in the preceding slides

is known as ID3 - Due to Quinlan (1986)
- Tends to choose attributes with many values
- Using information gain ratio helps solve this

problem - Several more improvements have been made to

handle numeric attributes (via univariate

splits), missing values and noisy data (via

pruning) - Resulting algorithm known as C4.5
- Described by Quinlan (1993)
- Widely used (as is the commercial version C5.0)
- WEKA has a version called J4.8

Classification Trees

- Described (along with regression trees) in
- L. Breiman, J.H. Friedman, R.A. Olshen, and C.J.

Stone, 1984. Classification and Regression Trees. - More sophisticated method than ID3
- However Quinlans (1993) C4.5 method caught up

with CART in most areas - CART also incorporates methods for pruning,

missing values and numeric attributes - Multivariate splits are possible, as well as

univariate - Split on linear combination Scjxj d
- CART typically uses Gini measure of node purity

to determine best splits - This is of the form Sp(1-p)
- But information/entropy measure also available

Regression Trees

- Trees can also be used to predict numeric

attributes - Predict using average value of the response in

the appropriate node - Implemented in CART and C4.5 frameworks
- Can use a model at each node instead
- Implemented in Wekas M5 algorithm
- Harder to interpret than regression trees
- Classification and regression trees are

implemented in Rs rpart package - See Ch 10 in Venables and Ripley, MASS 3rd Ed.

Problems with Trees

- Can be unnecessarily verbose
- Structure often unstable
- Greedy hierarchical algorithm
- Small variations can change chosen splits at high

level nodes, which then changes subtree below - Conclusions about attribute importance can be

unreliable - Direct methods tend to overfit training dataset
- This problem can be reduced by pruning the tree
- Another approach that often works well is to fit

the tree, remove all training cases that are not

correctly predicted, and refit the tree on the

reduced dataset - Typically gives a smaller tree
- This usually works almost as well on the training

data - But generalises better, e.g. works better on test

data - Bagging the tree algorithm also gives more stable

results - Will discuss bagging later

Classification Tree Example

- Use Wekas J4.8 algorithm on German credit data

(with default options) - 1000 instances, 21 attributes
- Produces a pruned tree with 140 nodes, 103 leaves

- Run information
- Scheme weka.classifiers.j48.J48 -C 0.25 -M

2 - Relation german_credit
- Instances 1000
- Attributes 21
- Number of Leaves 103
- Size of the tree 140
- Stratified cross-validation
- Summary
- Correctly Classified Instances 739

73.9 - Incorrectly Classified Instances 261

26.1 - Kappa statistic 0.3153
- Mean absolute error 0.3241
- Root mean squared error 0.4604

Cross-Validation

- Due to over-fitting, cannot estimate prediction

error directly on the training dataset - Cross-validation is a simple and widely used

method for estimating prediction error - Simple approach
- Set aside a test dataset
- Train learner on the remainder (the training

dataset) - Estimate prediction error by using the resulting

prediction model on the test dataset - This is only feasible where there is enough data

to set aside a test dataset and still have enough

to reliably train the learning algorithm

k-fold Cross-Validation

- For smaller datasets, use k-fold cross-validation
- Split dataset into k roughly equal parts
- For each part, train on the other k-1 parts and

use this part as the test dataset - Do this for each of the k parts, and average the

resulting prediction errors - This method measures the prediction error when

training the learner on a fraction (k-1)/k of the

data - If k is small, this will overestimate the

prediction error - k10 is usually enough

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Test

Regression Tree Example

- data(car.test.frame)
- z.auto
- post(z.auto,FILE)
- summary(z.auto)

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- Call
- rpart(formula Mileage Weight, data

car.test.frame) - n 60
- CP nsplit rel error xerror

xstd - 1 0.59534912 0 1.0000000 1.0322233

0.17981796 - 2 0.13452819 1 0.4046509 0.6081645

0.11371656 - 3 0.01282843 2 0.2701227 0.4557341

0.09178782 - 4 0.01000000 3 0.2572943 0.4659556

0.09134201 - Node number 1 60 observations, complexity

param0.5953491 - mean24.58333, MSE22.57639
- left son2 (45 obs) right son3 (15 obs)
- Primary splits
- Weight improve0.5953491, (0 missing)
- Node number 2 45 observations, complexity

param0.1345282 - mean22.46667, MSE8.026667
- left son4 (22 obs) right son5 (23 obs)

- Node number 3 15 observations
- mean30.93333, MSE12.46222
- Node number 4 22 observations
- mean20.40909, MSE2.78719
- Node number 5 23 observations, complexity

param0.01282843 - mean24.43478, MSE5.115312
- left son10 (15 obs) right son11 (8 obs)
- Primary splits
- Weight improve0.1476996, (0 missing)
- Node number 10 15 observations
- mean23.8, MSE4.026667
- Node number 11 8 observations
- mean25.625, MSE4.984375

Regression Tree Example (continued)

- plotcp(z.auto)
- z2.auto
- post(z2.auto, file"", cex1)

Complexity Parameter Plot

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Pruned Regression Tree

Classification Methods

- Project the attribute space into decision regions
- Decision trees piecewise constant approximation
- Logistic regression linear log-odds

approximation - Discriminant analysis and neural nets linear

non-linear separators - Density estimation coupled with a decision rule
- E.g. Naïve Bayes
- Define a metric space and decide based on

proximity - One type of instance-based learning
- K-nearest neighbour methods
- IBk algorithm in Weka
- Would like to drop noisy and unnecessary points
- Simple algorithm based on success rate confidence

intervals available in Weka - Compares naïve prediction with predictions using

that instance - Must choose suitable acceptance and rejection

confidence levels - Many of these approaches can produce probability

distributions as well as predictions - Depending on the application, this information

may be useful - Such as when results reported to expert (e.g.

loan officer) as input to their decision

Numeric Prediction Methods

- Linear regression
- Splines, including smoothing splines and

multivariate adaptive regression splines (MARS) - Generalised additive models (GAM)
- Locally weighted regression (lowess, loess)
- Regression and Model Trees
- CART, C4.5, M5
- Artificial neural networks (ANNs)

Artificial Neural Networks (ANNs)

- An ANN is a network of many simple processors (or

units), that are connected by communication

channels that carry numeric data - ANNs are very flexible, encompassing nonlinear

regression models, discriminant models, and data

reduction models - They do require some expertise to set up
- An appropriate architecture needs to be selected

and tuned for each application - They can be useful tools for learning from

examples to find patterns in data and predict

outputs - However on their own, they tend to overfit the

training data - Meta-learning tools are needed to choose the best

fit - Various network architectures in common use
- Multilayer perceptron (MLR)
- Radial basis functions (RBF)
- Self-organising maps (SOM)
- ANNs have been applied to data editing and

imputation, but not widely

Meta-Learning Methods - Bagging

- General methods for improving the performance of

most learning algorithms - Bootstrap aggregation, bagging for short
- Select B bootstrap samples from the data
- Selected with replacement, same of instances
- Can use parametric or non-parametric bootstrap
- Fit the model/learner on each bootstrap sample
- The bagged estimate is the average prediction

from all these B models - E.g. for a tree learner, the bagged estimate is

the average prediction from the resulting B trees - Note that this is not a tree
- In general, bagging a model or learner does not

produce a model or learner of the same form - Bagging reduces the variance of unstable

procedures like regression trees, and can greatly

improve prediction accuracy - However it does not always work for poor 0-1

predictors

Meta-Learning Methods - Boosting

- Boosting is a powerful technique for improving

accuracy - The AdaBoost.M1 method (for classifiers)
- Give each instance an initial weight of 1/n
- For m1 to M
- Fit model using the current weights, store

resulting model m - If prediction error rate err is zero or 0.5,

terminate loop. - Otherwise calculate amlog((1-err)/err)
- This is the log odds of success
- Then adjust weights for incorrectly classified

cases by multiplying them by exp(am), and repeat - Predict using a weighted majority vote SamGm(x),

where Gm(x) is the prediction from model m

Meta-Learning Methods - Boosting

- For example, for the German credit dataset
- using 100 iterations of AdaBoost.M1 with the

DecisionStump algorithm, - 10-fold cross-validation gives an error rate of

24.9 (compared to 26.1 for J4.8)

Association Rules

- Data on n purchase baskets in form (id, item1,

item2, , itemk) - For example, purchases from a supermarket
- Association rules are statements of the form
- When people buy tea, they also often buy

coffee. - May be useful for product placement decisions or

cross-selling recommendations - We say there is an association rule i1 -i2 if
- i1 and i2 occur together in at least s of the n

baskets (the support) - And at least c of the baskets containing item i1

also contain i2 (the confidence) - The confidence criterion ensures that often is

a large enough proportion of the antecedent cases

to be interesting - The support criterion should be large enough that

the resulting rules have practical importance - Also helps to ensure reliability of the

conclusions

Association rules

- The support/confidence approach is widely used
- Efficiently implemented in the Apriori algorithm
- First identify item sets with sufficient support
- Then turn each item set into sets of rules with

sufficient confidence - This method was originally developed in the

database community, so there has been a focus on

efficient methods for large databases - Large means up to around 100 million instances,

and about ten thousand binary attributes - However this approach can find a vast number of

rules, and it can be difficult to make sense of

these - One useful extension is to identify only the

rules with high enough lift (or odds ratio)

Classification vs Association Rules

- Classification rules predict the value of a

pre-specified attribute, e.g. - If outlooksunny and humidityhigh then play no
- Association rules predict the value of an

arbitrary attribute (or combination of

attributes) - E.g. If temperaturecool then humiditynormal
- If humiditynormal and playno then windytrue
- If temperaturehigh and humidityhigh then playno

Clustering EM Algorithm

- Assume that the data is from a mixture of normal

distributions - I.e. one normal component for each cluster
- For simplicity, consider one attribute x and two

components or clusters - Model has five parameters (p, µ1, s1, µ2, s2)

? - Log-likelihood
- This is hard to maximise directly
- Use the expectation-maximisation (EM) algorithm

instead

Clustering EM Algorithm

- Think of data as being augmented by a latent 0/1

variable di indicating membership of cluster 1 - If the values of this variable were known, the

log-likelihood would be - Starting with initial values for the parameters,

calculate the expected value of di - Then substitute this into the above

log-likelihood and maximise to obtain new

parameter values - This will have increased the log-likelihood
- Repeat until the log-likelihood converges

Clustering EM Algorithm

- Resulting estimates may only be a local maximum
- Run several times with different starting points

to find global maximum (hopefully) - With parameter estimates, can calculate segment

membership probabilities for each case

Clustering EM Algorithm

- Extending to more latent classes is easy
- Information criteria such as AIC and BIC are

often used to decide how many are appropriate - Extending to multiple attributes is easy if we

assume they are independent, at least

conditioning on segment membership - It is possible to introduce associations, but

this can rapidly increase the number of

parameters required - Nominal attributes can be accommodated by

allowing different discrete distributions in each

latent class, and assuming conditional

independence between attributes - Can extend this approach to a handle joint

clustering and prediction models, as mentioned in

the MVA lectures

Clustering - Scalability Issues

- k-means algorithm is also widely used
- However this and the EM-algorithm are slow on

large databases - So is hierarchical clustering - requires O(n2)

time - Iterative clustering methods require full DB scan

at each iteration - Scalable clustering algorithms are an area of

active research - A few recent algorithms
- Distance-based/k-Means
- Multi-Resolution kd-Tree for K-Means PM99
- CLIQUE AGGR98
- Scalable K-Means BFR98a
- CLARANS NH94
- Probabilistic/EM
- Multi-Resolution kd-Tree for EM Moore99
- Scalable EM BRF98b
- CF Kernel Density Estimation ZRL99

Ethics of Data Mining

- Data mining and data warehousing raise ethical

and legal issues - Combining information via data warehousing could

violate Privacy Act - Must tell people how their information will be

used when the data is obtained - Data mining raises ethical issues mainly during

application of results - E.g. using ethnicity as a factor in loan approval

decisions - E.g. screening job applications based on age or

sex (where not directly relevant) - E.g. declining insurance coverage based on

neighbourhood if this is related to race

(red-lining is illegal in much of the US) - Whether something is ethical depends on the

application - E.g. probably ethical to use ethnicity to

diagnose and choose treatments for a medical

problem, but not to decline medical insurance

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