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Data Mining In Market Research


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Data mining in market research

Data mining in market research

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  • 1. 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
  • 2. CRISP-DM Process Model
  • 3. Data Mining Software
    • Many providers of data mining software
      • SAS Enterprise Miner, SPSS Clementine, Statistica Data Miner, MS SQL Server, Polyanalyst, KnowledgeSTUDIO, …
      • See for a list
      • Good algorithms important, but also need good facilities for handling data and meta-data
    • We’ll 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 Implementations.
      • R packages
        • E.g. rpart, class, tree, nnet, cclust, deal, GeneSOM, knnTree, mlbench, randomForest, subselect
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. ARFF file for the weather data.
  • 9. 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
      • and on 747 web page
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. Naïve Bayes Calculations
    • Observed counts and probabilities above
      • Temperature and humidity have been discretised
    • Consider new day
      • Outlook=sunny, temperature=cool, humidity=high, windy=true
      • Probability(play=yes) α 2/9 x 3/9 x 3/9 x 3/9 x 9/14= 0.0053
      • Probability(play=no) α 3/5 x 1/5 x 4/5 x 3/5 x 5/14= 0.0206
      • Probability(play=no) = 0.0206/(0.0053+0.0206) = 79.5%
        • “ no” four times more likely than “yes”
  • 15. 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 cell’s 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
  • 16. 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- /
  • 17. Decision tree with a replicated subtree If x=1 and y=1 then class = a If z=1 and w=1 then class = a Otherwise class = b
  • 18. Problems with Univariate Splits
  • 19. 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
  • 20. Tree stumps for the weather data (a) (b) (c) (d)
  • 21. Weather Example
    • First node from outlook split is for “sunny”, with entropy – 2/5 * log 2 (2/5) – 3/5 * log 2 (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
  • 22. Expanded tree stumps for weather data (a) (b) (c)
  • 23. Decision tree for the weather data
  • 24. 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
  • 25. 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 Quinlan’s (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 Σ c j x j > d
      • CART typically uses Gini measure of node purity to determine best splits
        • This is of the form Σ p (1- p )
      • But information/entropy measure also available
  • 26. 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 Weka’s M5’ algorithm
        • Harder to interpret than regression trees
    • Classification and regression trees are implemented in R’s rpart package
      • See Ch 10 in Venables and Ripley, MASS 3 rd Ed.
  • 27. 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
  • 28. Classification Tree Example
    • Use Weka’s J4.8 algorithm on German credit data (with default options)
      • 1000 instances, 21 attributes
    • Produces a pruned tree with 140 nodes, 103 leaves
  • 29.
    • === 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
    • Relative absolute error 77.134 %
    • Root relative squared error 100.4589 %
    • Total Number of Instances 1000
    • === Detailed Accuracy By Class ===
    • TP Rate FP Rate Precision Recall F-Measure Class
    • 0.883 0.597 0.775 0.883 0.826 good
    • 0.403 0.117 0.596 0.403 0.481 bad
    • === Confusion Matrix ===
    • a b <-- classified as
    • 618 82 | a = good
    • 179 121 | b = bad
  • 30. 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
  • 31. 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
      • k =10 is usually enough
    Tr Tr Tr Tr Tr Tr Tr Tr Test
  • 32. Regression Tree Example
    • data(car.test.frame)
    • <- rpart(Mileage ~ Weight, car.test.frame)
    • post(,FILE=“”)
    • summary(
  • 33.  
  • 34.
    • 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 param=0.5953491
    • mean=24.58333, MSE=22.57639
    • left son=2 (45 obs) right son=3 (15 obs)
    • Primary splits:
    • Weight < 2567.5 to the right, improve=0.5953491, (0 missing)
    • Node number 2: 45 observations, complexity param=0.1345282
    • mean=22.46667, MSE=8.026667
    • left son=4 (22 obs) right son=5 (23 obs)
    • Primary splits:
    • Weight < 3087.5 to the right, improve=0.5045118, (0 missing)
    • … (continued on next page)…
  • 35.
    • Node number 3: 15 observations
    • mean=30.93333, MSE=12.46222
    • Node number 4: 22 observations
    • mean=20.40909, MSE=2.78719
    • Node number 5: 23 observations, complexity param=0.01282843
    • mean=24.43478, MSE=5.115312
    • left son=10 (15 obs) right son=11 (8 obs)
    • Primary splits:
    • Weight < 2747.5 to the right, improve=0.1476996, (0 missing)
    • Node number 10: 15 observations
    • mean=23.8, MSE=4.026667
    • Node number 11: 8 observations
    • mean=25.625, MSE=4.984375
  • 36. Regression Tree Example (continued)
    • plotcp(
    • <- prune(,cp=0.1)
    • post(, file=&quot;&quot;, cex=1)
  • 37. Complexity Parameter Plot
  • 38.  
  • 39. Pruned Regression Tree
  • 40. 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
  • 41. 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)
  • 42. 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
  • 43. 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
  • 44. 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 m =1 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 α m =log((1-err)/err)
          • This is the log odds of success
        • Then adjust weights for incorrectly classified cases by multiplying them by exp( α m ), and repeat
      • Predict using a weighted majority vote: Σ α m G m ( x ), where G m ( x ) is the prediction from model m
  • 45. 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)
  • 46. Association Rules
    • Data on n purchase baskets in form (id, item 1 , item 2 , …, item k )
      • 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 i 1 ->i 2 if
      • i 1 and i 2 occur together in at least s% of the n baskets (the support)
      • And at least c% of the baskets containing item i 1 also contain i 2 (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
  • 47. 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 i dentify only the rules with high enough lift (or odds ratio)
  • 48. Classification vs Association Rules
    • Classification rules predict the value of a pre-specified attribute, e.g.
        • If outlook=sunny and humidity=high then play =no
    • Association rules predict the value of an arbitrary attribute (or combination of attributes)
        • E.g. If temperature=cool then humidity=normal
        • If humidity=normal and play=no then windy=true
        • If temperature=high and humidity=high then play=no
  • 49. 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 , μ 1 , σ 1 , μ 2 , σ 2 ) = θ
    • Log-likelihood:
    • This is hard to maximise directly
      • Use the expectation-maximisation (EM) algorithm instead
  • 50. Clustering – EM Algorithm
    • Think of data as being augmented by a latent 0/1 variable d i 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 d i
    • 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
  • 51. 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
  • 52. 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
  • 53. 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( n 2 ) 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]
  • 54. 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
  • 55.