Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall)
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Transcript of Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall)
Decision Support and Decision Support and Business Intelligence Business Intelligence
SystemsSystems(9(9thth Ed., Prentice Hall) Ed., Prentice Hall)
Chapter 5:Chapter 5:
Data Mining for Business Data Mining for Business IntelligenceIntelligence
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Learning ObjectivesLearning Objectives Define data mining as an enabling
technology for business intelligence Understand the objectives and benefits of
business analytics and data mining Recognize the wide range of applications of
data mining Learn the standardized data mining
processes CRISP-DM, SEMMA, KDD, …
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Learning ObjectivesLearning Objectives
Understand the steps involved in data preprocessing for data mining
Learn different methods and algorithms of data mining
Build awareness of the existing data mining software tools Commercial versus free/open source
Understand the pitfalls and myths of data mining
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5.1. Opening Vignette:5.1. Opening Vignette:
“Data Mining Goes to Hollywood!” Decision situation Problem Proposed solution Results Answer and discuss the case
questions
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Opening Vignette:Opening Vignette:Data Mining Goes to Hollywood!Data Mining Goes to Hollywood!
Independent Variable Number of Values
Possible Values
MPAA Rating 5 G, PG, PG-13, R, NR
Competition 3 High, Medium, Low
Star value 3 High, Medium, Low
Genre 10
Sci-Fi, Historic Epic Drama, Modern Drama, Politically Related, Thriller, Horror, Comedy, Cartoon, Action, Documentary
Special effects 3 High, Medium, Low
Sequel 1 Yes, No
Number of screens 1 Positive integer
Class No. 1 2 3 4 5 6 7 8 9
Range
(in $Millions)
< 1
(Flop)
> 1
< 10
> 10
< 20
> 20
< 40
> 40
< 65
> 65
< 100
> 100
< 150
> 150
< 200
> 200
(Blockbuster)
DependeDependent nt
VariableVariable IndependeIndependent nt
VariablesVariables
A Typical A Typical Classification Classification
ProblemProblem
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Opining Vignette:Opining Vignette:Data Mining Goes to Hollywood!Data Mining Goes to Hollywood!
ModelDevelopmentprocess
ModelAssessmentprocess
The DM The DM Process Process Map in Map in PASWPASW
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Opening Vignette:Opening Vignette:Data Mining Goes to Hollywood!Data Mining Goes to Hollywood!
Prediction Models
Individual Models Ensemble Models
Performance Measure SVM ANN C&RT
Random Forest
Boosted Tree
Fusion (Average)
Count (Bingo) 192 182 140 189 187 194
Count (1-Away) 104 120 126 121 104 120
Accuracy (% Bingo) 55.49% 52.60% 40.46% 54.62% 54.05% 56.07%
Accuracy (% 1-Away) 85.55% 87.28% 76.88% 89.60% 84.10% 90.75%
Standard deviation 0.93 0.87 1.05 0.76 0.84 0.63
* Training set: 1998 – 2005 movies; Test set: 2006 movies
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5.1. Data mining concepts and 5.1. Data mining concepts and applicationsapplications
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Why Data Mining?Why Data Mining? More intense competition at the global scale Recognition of the value in data sources Availability of quality data on customers,
vendors, transactions, Web, etc. Consolidation and integration of data
repositories into data warehouses The exponential increase in data processing
and storage capabilities; and decrease in cost
Movement toward conversion of information resources into nonphysical form
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Definition of Data MiningDefinition of Data Mining The nontrivial process of identifying valid,
novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. - Fayyad et al., (1996)
Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable.
Data mining: a misnomer? Other names: knowledge extraction, pattern
analysis, knowledge discovery, information harvesting, pattern searching, data dredging,…
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Data Mining at the Intersection Data Mining at the Intersection of Many Disciplinesof Many Disciplines
Management Science & Information Systems
Databases
Pattern Recognition
MachineLearning
MathematicalModeling
DATAMINING
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Data Mining Data Mining Characteristics/ObjectivesCharacteristics/Objectives Source of data for DM is often a consolidated
data warehouse (not always!) DM environment is usually a client-server or a
Web-based information systems architecture Data is the most critical ingredient for DM
which may include soft/unstructured data The miner is often an end user Striking it rich requires creative thinking Data mining tools’ capabilities and ease of use
are essential (Web, Parallel processing, etc.)
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Data in Data MiningData in Data Mining
Data
Categorical Numerical
Nominal Ordinal Interval Ratio
Data: a collection of facts usually obtained as the result of experiences, observations, or experiments
Data may consist of numbers, words, images, … Data: lowest level of abstraction (from which
information and knowledge are derived)- DM with
different data types?
- Other data types?
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What Does DM Do?What Does DM Do?
DM extract patterns from data Pattern? A mathematical (numeric
and/or symbolic) relationship among data items
Types of patterns Association Prediction Cluster (segmentation) Sequential (or time series) relationships
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A Taxonomy for Data Mining A Taxonomy for Data Mining TasksTasks
Data Mining
Prediction
Classification
Regression
Clustering
Association
Link analysis
Sequence analysis
Learning Method Popular Algorithms
Supervised
Supervised
Supervised
Unsupervised
Unsupervised
Unsupervised
Unsupervised
Decision trees, ANN/MLP, SVM, Rough sets, Genetic Algorithms
Linear/Nonlinear Regression, Regression trees, ANN/MLP, SVM
Expectation Maximization, Apriory Algorithm, Graph-based Matching
Apriory Algorithm, FP-Growth technique
K-means, ANN/SOM
Outlier analysis Unsupervised K-means, Expectation Maximization (EM)
Apriory, OneR, ZeroR, Eclat
Classification and Regression Trees, ANN, SVM, Genetic Algorithms
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Data Mining Tasks (cont.) Data Mining Tasks (cont.)
Time-series forecasting Part of sequence or link analysis?
Visualization Another data mining task?
Types of DM Hypothesis-driven data mining Discovery-driven data mining
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5.3. Data Mining Applications5.3. Data Mining Applications Customer Relationship Management
(CRM) Maximize return on marketing campaigns Improve customer retention (churn analysis) Maximize customer value (cross-, up-selling) Identify and treat most valued customers
Banking and Other Financial Automate the loan application process Detecting fraudulent transactions Maximize customer value (cross-, up-selling) Optimizing cash reserves with forecasting
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Data Mining Applications (cont.)Data Mining Applications (cont.) Retailing and Logistics
Optimize inventory levels at different locations Improve the store layout and sales promotions Optimize logistics by predicting seasonal effects Minimize losses due to limited shelf life
Manufacturing and Maintenance Predict/prevent machinery failures Identify anomalies in production systems to
optimize the use manufacturing capacity Discover novel patterns to improve product
quality
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Data Mining Applications (cont.)Data Mining Applications (cont.) Brokerage and Securities Trading
Predict changes on certain bond prices Forecast the direction of stock fluctuations Assess the effect of events on market movements Identify and prevent fraudulent activities in trading
Insurance Forecast claim costs for better business planning Determine optimal rate plans Optimize marketing to specific customers Identify and prevent fraudulent claim activities
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Data Mining Applications (cont.)Data Mining Applications (cont.)
Computer hardware and software Science and engineering Government and defense Homeland security and law enforcement Travel industry Healthcare Medicine Entertainment industry Sports Etc.
Highly popular application areas for data mining
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5.4. Data Mining Process5.4. Data Mining Process
A manifestation of best practices A systematic way to conduct DM
projects Different groups has different versions Most common standard processes:
CRISP-DM (Cross-Industry Standard Process for Data Mining)
SEMMA (Sample, Explore, Modify, Model, and Assess)
KDD (Knowledge Discovery in Databases)
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Data Mining ProcessData Mining Process
Source: KDNuggets.com, August 2007
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Data Mining Process: Data Mining Process: CRISP-DMCRISP-DM
Data Sources
Business Understanding
Data Preparation
Model Building
Testing and Evaluation
Deployment
Data Understanding
6
1 2
3
5
4
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Data Mining Process: Data Mining Process: CRISP-DM (cont.)CRISP-DM (cont.)
Step 1: Business UnderstandingStep 2: Data UnderstandingStep 3: Data Preparation (!)Step 4: Model BuildingStep 5: Testing and EvaluationStep 6: Deployment
The process is highly repetitive and experimental (DM: art versus science?)
Accounts for ~85% of
total project time
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Data Preparation – Data Preparation – A Critical DM TaskA Critical DM Task
Data Consolidation
Data Cleaning
Data Transformation
Data Reduction
Well-formedData
Real-worldData
· Collect data· Select data· Integrate data
· Impute missing values· Reduce noise in data · Eliminate inconsistencies
· Normalize data· Discretize/aggregate data · Construct new attributes
· Reduce number of variables· Reduce number of cases · Balance skewed data
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Data Mining Process: Data Mining Process: SEMMASEMMA
Sample
(Generate a representative sample of the data)
Modify(Select variables, transform
variable representations)
Explore(Visualization and basic description of the data)
Model(Use variety of statistical and machine learning models )
Assess(Evaluate the accuracy and usefulness of the models)
SEMMA
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5.5. Data mining methods5.5. Data mining methods
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Data Mining Methods: Data Mining Methods: ClassificationClassification
Most frequently used DM method Part of the machine-learning family Employ supervised learning Learn from past data, classify new
data The output variable is categorical
(nominal or ordinal) in nature Classification versus regression? Classification versus clustering?
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Assessment Methods for Assessment Methods for ClassificationClassification
Predictive accuracy Hit rate
Speed Model building; predicting
Robustness Scalability Interpretability
Transparency, explainability
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Accuracy of Classification ModelsAccuracy of Classification Models In classification problems, the primary source
for accuracy estimation is the confusion matrix
True Positive
Count (TP)
FalsePositive
Count (FP)
TrueNegative
Count (TN)
FalseNegative
Count (FN)
True Class
Positive Negative
Pos
itive
Neg
ativ
e
Pre
dict
ed C
lass FNTP
TPRatePositiveTrue
FPTN
TNRateNegativeTrue
FNFPTNTP
TNTPAccuracy
FPTP
TPrecision
P
FNTP
TPcallRe
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Estimation Methodologies for Estimation Methodologies for ClassificationClassification
Simple split (or holdout or test sample estimation) Split the data into 2 mutually exclusive sets
training (~70%) and testing (30%)
For ANN, the data is split into three sub-sets (training [~60%], validation [~20%], testing [~20%])
PreprocessedData
Training Data
Testing Data
Model Development
Model Assessment
(scoring)
2/3
1/3
Classifier
Prediction Accuracy
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Estimation Methodologies for Estimation Methodologies for ClassificationClassification k-Fold Cross Validation (rotation
estimation) Split the data into k mutually exclusive subsets Use each subset as testing while using the rest
of the subsets as training Repeat the experimentation for k times Aggregate the test results for true estimation
of prediction accuracy training Other estimation methodologies
Leave-one-out, bootstrapping, jackknifing Area under the ROC curve
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Estimation Methodologies for Estimation Methodologies for Classification – ROC CurveClassification – ROC Curve
10.90.80.70.60.50.40.30.20.10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1
0.9
0.8
False Positive Rate (1 - Specificity)
Tru
e P
ositi
ve R
ate
(Sen
sitiv
ity) A
B
C
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Classification TechniquesClassification Techniques
Decision tree analysis Statistical analysis Neural networks Support vector machines Case-based reasoning Bayesian classifiers Genetic algorithms Rough sets
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Decision TreesDecision Trees Employs the divide and conquer method Recursively divides a training set until each
division consists of examples from one class1. Create a root node and assign all of the training
data to it2. Select the best splitting attribute3. Add a branch to the root node for each value of
the split. Split the data into mutually exclusive subsets along the lines of the specific split
4. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached
A general algorithm for decision tree building
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Decision Trees Decision Trees
DT algorithms mainly differ on Splitting criteria
Which variable to split first? What values to use to split? How many splits to form for each node?
Stopping criteria When to stop building the tree
Pruning (generalization method) Pre-pruning versus post-pruning
Most popular DT algorithms include ID3, C4.5, C5; CART; CHAID; M5
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Decision TreesDecision Trees
Alternative splitting criteria Gini index determines the purity of a
specific class as a result of a decision to branch along a particular attribute/value
Used in CART Information gain uses entropy to
measure the extent of uncertainty or randomness of a particular attribute/value split
Used in ID3, C4.5, C5 Chi-square statistics (used in CHAID)
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Cluster Analysis for Data MiningCluster Analysis for Data Mining
Used for automatic identification of natural groupings of things
Part of the machine-learning family Employ unsupervised learning Learns the clusters of things from
past data, then assigns new instances
There is not an output variable Also known as segmentation
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Cluster Analysis for Data MiningCluster Analysis for Data Mining
Clustering results may be used to Identify natural groupings of customers Identify rules for assigning new cases to
classes for targeting/diagnostic purposes
Provide characterization, definition, labeling of populations
Decrease the size and complexity of problems for other data mining methods
Identify outliers in a specific domain (e.g., rare-event detection)
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Cluster Analysis for Data MiningCluster Analysis for Data Mining
Analysis methods Statistical methods (including both
hierarchical and nonhierarchical), such as k-means, k-modes, and so on
Neural networks (adaptive resonance theory [ART], self-organizing map [SOM])
Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms
Divisive versus Agglomerative methods
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Cluster Analysis for Data MiningCluster Analysis for Data Mining How many clusters?
There is not a “truly optimal” way to calculate it Heuristics are often used
Look at the sparseness of clusters Number of clusters = (n/2)1/2 (n: no of data points)
Use Akaike information criterion (AIC) Use Bayesian information criterion (BIC)
Most cluster analysis methods involve the use of a distance measure to calculate the closeness between pairs of items Euclidian versus Manhattan (rectilinear)
distance
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Cluster Analysis for Data MiningCluster Analysis for Data Mining
k-Means Clustering Algorithm k : pre-determined number of clusters Algorithm (Step 0: determine value of k)
Step 1: Randomly generate k random points as initial cluster centers
Step 2: Assign each point to the nearest cluster center
Step 3: Re-compute the new cluster centersRepetition step: Repeat steps 3 and 4 until
some convergence criterion is met (usually that the assignment of points to clusters becomes stable)
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Cluster Analysis for Data Mining Cluster Analysis for Data Mining - - kk-Means Clustering Algorithm-Means Clustering Algorithm
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Association Rule MiningAssociation Rule Mining A very popular DM method in business Finds interesting relationships (affinities)
between variables (items or events) Part of machine learning family Employs unsupervised learning There is no output variable Also known as market basket analysis Often used as an example to describe DM
to ordinary people, such as the famous “relationship between diapers and beers!”
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Association Rule MiningAssociation Rule Mining Input: the simple point-of-sale transaction data Output: Most frequent affinities among items Example: according to the transaction data…
“Customer who bought a laptop computer and a virus protection software, also bought extended service plan 70 percent of the time."
How do you use such a pattern/knowledge? Put the items next to each other for ease of finding Promote the items as a package (do not put one on
sale if the other(s) are on sale) Place items far apart from each other so that the
customer has to walk the aisles to search for it, and by doing so potentially seeing and buying other items
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Association Rule MiningAssociation Rule Mining
A representative applications of association rule mining include In business: cross-marketing, cross-selling,
store design, catalog design, e-commerce site design, optimization of online advertising, product pricing, and sales/promotion configuration
In medicine: relationships between symptoms and illnesses; diagnosis and patient characteristics and treatments (to be used in medical DSS); and genes and their functions (to be used in genomics projects)…
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Association Rule MiningAssociation Rule Mining Are all association rules interesting and
useful?
A Generic Rule: X Y [S%, C%]
X, Y: products and/or services X: Left-hand-side (LHS)Y: Right-hand-side (RHS)S: Support: how often X and Y go togetherC: Confidence: how often Y go together with the X
Example: {Laptop Computer, Antivirus Software} {Extended Service Plan} [30%, 70%]
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Association Rule MiningAssociation Rule Mining
Algorithms are available for generating association rules Apriori Eclat FP-Growth + Derivatives and hybrids of the three
The algorithms help identify the frequent item sets, which are, then converted to association rules
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Association Rule MiningAssociation Rule Mining
Apriori Algorithm Finds subsets that are common to at
least a minimum number of the itemsets uses a bottom-up approach
frequent subsets are extended one item at a time (the size of frequent subsets increases from one-item subsets to two-item subsets, then three-item subsets, and so on), and
groups of candidates at each level are tested against the data for minimum support
see the figure…
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Association Rule MiningAssociation Rule Mining
Apriori Algorithm
Itemset(SKUs)
SupportTransaction
NoSKUs
(Item No)
1
1
1
1
1
1
1, 2, 3, 4
2, 3, 4
2, 3
1, 2, 4
1, 2, 3, 4
2, 4
Raw Transaction Data
1
2
3
4
3
6
4
5
Itemset(SKUs)
Support
1, 2
1, 3
1, 4
2, 3
3
2
3
4
3, 4
5
3
2, 4
Itemset(SKUs)
Support
1, 2, 4
2, 3, 4
3
3
One-item Itemsets Two-item Itemsets Three-item Itemsets
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5.6. Data Mining 5.6. Data Mining SoftwareSoftware
Commercial SPSS - PASW (formerly
Clementine) SAS - Enterprise Miner IBM - Intelligent Miner StatSoft – Statistical
Data Miner … many more
Free and/or Open Source Weka RapidMiner… 0 20 40 60 80 100 120
Thinkanalytics
Miner3D
Clario Analytics
Viscovery
Megaputer
Insightful Miner/S-Plus (now TIBCO)
Bayesia
C4.5, C5.0, See5
Angoss
Orange
Salford CART, Mars, other
Statsoft Statistica
Oracle DM
Zementis
Other free tools
Microsoft SQL Server
KNIME
Other commercial tools
MATLAB
KXEN
Weka (now Pentaho)
Your own code
R
Microsoft Excel
SAS / SAS Enterprise Miner
RapidMiner
SPSS PASW Modeler (formerly Clementine)
Total (w/ others) Alone
Source: KDNuggets.com, May 2009
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Data Mining MythsData Mining Myths
Data mining … provides instant solutions/predictions is not yet viable for business applications requires a separate, dedicated database can only be done by those with advanced
degrees is only for large firms that have lots of
customer data is another name for the good-old statistics
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Common Data Mining MistakesCommon Data Mining Mistakes
1. Selecting the wrong problem for data mining
2. Ignoring what your sponsor thinks data mining is and what it really can/cannot do
3. Not leaving insufficient time for data acquisition, selection and preparation
4. Looking only at aggregated results and not at individual records/predictions
5. Being sloppy about keeping track of the data mining procedure and results
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Common Data Mining MistakesCommon Data Mining Mistakes
6. Ignoring suspicious (good or bad) findings and quickly moving on
7. Running mining algorithms repeatedly and blindly, without thinking about the next stage
8. Naively believing everything you are told about the data
9. Naively believing everything you are told about your own data mining analysis
10. Measuring your results differently from the way your sponsor measures them
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ToolsTools
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Tools and softwareTools and software
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Clementine (SPSS Modeler), by IBMClementine (SPSS Modeler), by IBM
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Enterprise Miner, By SASEnterprise Miner, By SAS
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Statistica data minerStatistica data miner
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Intelligent minerIntelligent miner
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PolyAnalystPolyAnalyst
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CART, MARS, TreeNet, CART, MARS, TreeNet, RandomForestRandomForest
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Insightful MinerInsightful Miner
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XL MinerXL Miner
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KXEN (KXEN (KKnowledge enowledge eXXtraction traction ENENgines)gines)
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Ghost MinerGhost Miner
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Microsoft SQL server Data Microsoft SQL server Data MiningMining
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Knowledge MinerKnowledge Miner
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TeraData Warehouse MinerTeraData Warehouse Miner
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Oracle Data Mining (ODM)Oracle Data Mining (ODM)
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Fair Isaac Business ScienceFair Isaac Business Science
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Delta MasterDelta Master
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iData analyzeriData analyzer
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Zementis predictive analyticsZementis predictive analytics
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LIONSolverLIONSolver
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TanagraTanagra
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Orange Data Mining toolOrange Data Mining tool
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Rapid MinerRapid Miner
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WekaWeka
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jHepWorkjHepWork
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KMINEKMINE
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R Data MiningR Data Mining
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End of the Chapter End of the Chapter
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