data mining in market research
TRANSCRIPT
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 We’ll 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 dataset14 instances5 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.arff
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
),,(
,,CBAP
XPXCPXBPXAPCBAXP
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”
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
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 x=1 and y=1 then class = aIf z=1 and w=1 then class = aOtherwise 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
i
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iin ppppp 2
121 log,,,entropy
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 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 Σcjxj > d CART typically uses Gini measure of node purity to determine best
splits This is of the form Σp(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 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 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 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
=== 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
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 k=10 is usually enough
Tr Tr TrTrTrTrTrTrTest
Regression Tree Example
data(car.test.frame) z.auto <- rpart(Mileage ~ Weight,
car.test.frame) post(z.auto,FILE=“”) summary(z.auto)
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Weight>=2568
Weight>=3088
Weight>=2748
Weight< 2568
Weight< 3088
Weight< 2748
24.58n=60
22.47n=45
20.41n=22
24.43n=23
23.8n=15
25.63n=8
30.93n=15
Endpoint = Mileage
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)…
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
Regression Tree Example (continued) plotcp(z.auto) z2.auto <- prune(z.auto,cp=0.1) post(z2.auto, file="", cex=1)
Complexity Parameter Plot
cp
X-v
al R
ela
tive
Err
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0.4
0.6
0.8
1.0
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Inf 0.28 0.042 0.011
1 2 3 4
size of tree
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Endpoint = Mileage
Pruned Regression Tree|
Weight>=2568
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Endpoint = Mileage
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 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 αm=log((1-err)/err)
This is the log odds of success Then adjust weights for incorrectly classified cases by
multiplying them by exp(αm), and repeat Predict using a weighted majority vote: ΣαmGm(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 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
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, σ1, μ2, σ2) = θ
Log-likelihood:
This is hard to maximise directly Use the expectation-maximisation (EM) algorithm instead
component. for thedensity normal theis where
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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
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Clustering – EM Algorithm
Resulting estimates may only be a local maximumRun several times with different starting points
to find global maximum (hopefully) With parameter estimates, can calculate
segment membership probabilities for each case
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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
