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Data Mining in Market Research


Data Mining in Market Research What is data mining? Methods for finding interesting structure in large databases E.g. patterns, prediction rules, unusual cases – PowerPoint PPT presentation

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Date added: 10 April 2020
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Title: Data Mining in Market Research

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
  • 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
  • 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,
  • See http//
    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
  • Witten and Frank, 2000. Data Mining Practical
    Machine Learning Tools and Techniques with Java
  • 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

  • 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
  • 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
  • http//
  • and on 747 web page
  • http//

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
  • 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
  • Common method is to split at each change in the
  • Collapse buckets until each contains at least 6

1R Algorithm (continued)
  • Biased towards predictors with more categories
  • These can result in over-fitting to the training
  • 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
  • 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,
  • 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)
  • 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
  • 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
  • However this method often works well in practice

Decision Trees
  • Classification rules can be expressed in a tree
  • 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.

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
  • 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
Weather Example
  • First node from outlook split is for sunny,
    with entropy 2/5 log2(2/5) 3/5 log2(3/5)
  • 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
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
  • Several more improvements have been made to
    handle numeric attributes (via univariate
    splits), missing values and noisy data (via
  • 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
  • Split on linear combination Scjxj gt 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
  • 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
  • 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
  • But generalises better, e.g. works better on test
  • Bagging the tree algorithm also gives more stable
  • 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
  • Relation german_credit
  • Instances 1000
  • Attributes 21
  • Number of Leaves 103
  • Size of the tree 140
  • Stratified cross-validation
  • Summary
  • Correctly Classified Instances 739
  • Incorrectly Classified Instances 261
  • Kappa statistic 0.3153
  • Mean absolute error 0.3241
  • Root mean squared error 0.4604

  • 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
  • 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
  • If k is small, this will overestimate the
    prediction error
  • k10 is usually enough

Regression Tree Example
  • data(car.test.frame)
  • lt- rpart(Mileage Weight, car.test.frame)
  • post(,FILE)
  • summary(

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  • Call
  • rpart(formula Mileage Weight, data
  • n 60
  • CP nsplit rel error xerror
  • 1 0.59534912 0 1.0000000 1.0322233
  • 2 0.13452819 1 0.4046509 0.6081645
  • 3 0.01282843 2 0.2701227 0.4557341
  • 4 0.01000000 3 0.2572943 0.4659556
  • Node number 1 60 observations, complexity
  • mean24.58333, MSE22.57639
  • left son2 (45 obs) right son3 (15 obs)
  • Primary splits
  • Weight lt 2567.5 to the right,
    improve0.5953491, (0 missing)
  • Node number 2 45 observations, complexity
  • 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
  • mean24.43478, MSE5.115312
  • left son10 (15 obs) right son11 (8 obs)
  • Primary splits
  • Weight lt 2747.5 to the right,
    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(
  • lt- prune(,cp0.1)
  • post(, 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
  • 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
  • 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
  • However on their own, they tend to overfit the
    training data
  • Meta-learning tools are needed to choose the best
  • 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

Meta-Learning Methods - Boosting
  • Boosting is a powerful technique for improving
  • 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 gt 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
  • May be useful for product placement decisions or
    cross-selling recommendations
  • We say there is an association rule i1 -gti2 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

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
  • 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
  • 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
  • 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

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)
  • 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
  • Scalable K-Means BFR98a
  • 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
  • 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
  • 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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