A Comparison of Leading Data Mining Tools
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1 A Comparison of Leading Data Mining Tools John F. Elder IV & Dean W. Abbott Elder Research Fourth International Conference on Knowledge Discovery & Data Mining Friday, August 28, 1998 New York, New York
2 Copyright 1998, John F. Elder IV and Dean W. Abbott All rights reserved Manufactured in the United States of America 1998 Elder Research T8-2
3 Contacting Elder Research Dr. John F. Elder IV 1006 Wildmere Place Charlottesville, VA Dean W. Abbott 3443 Villanova Avenue San Diego, CA Fax: Elder Research T8-3
4 Tutorial Goals Compare and Summarize Data Mining Tools which: Offer multiple modeling and classification algorithms Support project stages surrounding model construction Stand alone Are general-purpose Cost a lot We could get our hands on Include some (focused) Desktop Tools Other Reports: Two Crows, Aberdeen Group, Elder Research (forthcoming ), Data Mining Journal 1998 Elder Research T8-4
5 Topics Products covered Review of algorithms Comparative tables of properties Screen shots exemplifying qualities Summary of distinctives 1998 Elder Research T8-5
6 Caveats We don t know every tool well (and are sure to have missed some!) Level of exposure noted for each tool Our background (biasing our perspective) Very technical, early adopters Emphasize solving real-world applications More classification than estimation Field of tools is quite dynamic New versions appear regularly 1998 Elder Research T8-6
7 Data Mining Products PRW Model Elder Research T8-7
8 Tools Evaluated Product Company URL Version Tested Our Experience Clementine Integral Solutions, Integral Ltd. Solutions, Ltd. 4 Moderate Darwin Thinking Machines, Corp. Corp Moderate DataCruncher DataMind High Enterprise Miner SAS Institute Beta Moderate GainSmarts Urban Science Low Intelligent Miner IBM 2 Low MineSet Silicon Graphics, Inc. Inc Low Model 1 Group 11/Unica Technologies Moderate ModelQuest AbTech Corp. 1 Moderate PRW Unica Technologies, Inc High CART Salford Systems Moderate NeuroShell Ward Systems Group, Inc. 3 Moderate OLPARS PAR Government Systems mailto://olpars@partech.com 8.1 High Scenario Cognos 2 Moderate See5 RuleQuest Research Moderate S-Plus MathSoft 4 High WizWhy WizSoft Moderate 1998 Elder Research T8-8
9 Categories for Comparisons Platforms Supported Algorithms Included Decision Trees Neural Networks Other Data Input and Model Output Options Usability Ratings Visualization Capabilities Modeling Automation Methods 1998 Elder Research T8-9
10 Platforms PC Standalone (95/NT) Unix Standalone Unix Server / PC Client 1998 Elder Research T8-10 NT Server / PC Client Database Connectivity Clementine + Darwin DataCruncher Enterprise Miner + GainSmarts Intelligent Miner MineSet Model 1 ModelQuest PRW CART + Scenario NeuroShell OLPARS See5 + S-Plus WizWhy Key blank no capability some capability good capability + excellent capability
11 Tool Groupings Desktop PC (standalone) Flat Files One or Two Algorithms High End Multiple Platforms, Client- Server Flat Files or Direct Database Access Multiple Algorithm Types Data Fits into RAM Large Databases 1998 Elder Research T8-11
12 End User Perspectives Business Intuitive Interface Clear steps in data mining process Non-technical terminology Familiar environment Descriptive Reporting Domain terminology Graphical representations Technical Algorithm Options Knobs to enhance model performance Model Automation Simplify model design cycle Documentation of steps used in generating models (repeatability) 1998 Elder Research T8-12
13 OLPARS: Interface 1998 Elder Research T8-13
14 MineSet: Interface 1998 Elder Research T8-14
15 PRW: Experiment Manager 1998 Elder Research T8-15
16 Intelligent Miner: Explorer Interface 1998 Elder Research T8-16
17 Enterprise Miner: Visual Interface 1998 Elder Research T8-17
18 Clementine: Visual Interface 1998 Elder Research T8-18
19 DataCruncher: Process Flow Diagram 1998 Elder Research T8-19
20 Data Input & Model Output Automatic Header Save Data Format ODBC Native Database Drivers Summary Reports Output Source Code Clementine Darwin DataCruncher Enterprise Miner GainSmarts Intelligent Miner MineSet Model 1 ModelQuest PRW CART Scenario NeuroShell OLPARS See5 S-Plus WizWhy 1998 Elder Research T8-20
21 PRW: Row Splitting 1998 Elder Research T8-21
22 Model1: Variable Selection 1998 Elder Research T8-22
23 Model1: Data Definitions 1998 Elder Research T8-23
24 Scenario : Special Data Types 1998 Elder Research T8-24
25 Decision Trees n a > 4 y n b>3.5 y n b > 2 y n a > 1 y Elder Research T8-25
26 Polynomial Networks Z 17 = a -.15b bc abc + 0.5c 3 Layer 0 (Normalizers) Layer 1 a N1 z1 z9 Double 16 k N9 z6 Single 14 f N6 d N4 z4 Triple 17 z16 z14 z17 Layer 2 Multi- Linear 15 z15 Layer 3 Triple 21 z21 Unitizers U2 Y1 h N8 z8 Double 20 z20 Double 19 z19 U7 Y2 e N5 z Elder Research T8-26
27 Consensus Models Parametrically Summarize Data Points orders, terms Regression Polynomial Networks (e.g. GMDH, ASPN) Decision Trees (e.g., CART, CHAID, C5) Logistic or Sigmoidal Networks (ANNs) Hinging Hyperplanes, MARS 1998 Elder Research T8-27
28 Consensus Models (continued) orientation, bin width Histogram function Radial Basis Function family, order Wavelets 1998 Elder Research T8-28
29 Contributory Models retain data points; each potentially affects estimate at new point shape, spread Kernels k, distance metric k-nearest Neighbor Goal, iterations Delaunay Planes Spread, index Projection Pursuit Regression 1998 Elder Research T8-29
30 Properties of Algorithms Algorithm Accurate Scalable Interpretable Useable Robust Versatile Fast Hot Classical (LR, LDA) Neural Networks Visualization Decision Trees Polynomial Networks K-Nearest Neighbors Kernels Key good neutral bad 1998 Elder Research T8-30
31 Algorithms Decision Trees Linear/Statistical Multi-layer Perceptrons Nearest Neighbor Radial Basis Functions Bayes Rule Induction Polynomial Networks Generalized Linear Models Time Series Sequential Discovery K Means Association Rules Kohonen Clementine Darwin Datamind Enterprise Miner GainSmarts + Intelligent Miner + MineSet Model 1 + ModelQuest PRW + CART Cognos NeuroShell + OLPARS See5 SPlus + WizWhy 1998 Elder Research T8-31
32 Multi-Layer Perceptrons Learning Rate Learning Rate Decay Momentum Multiple Activation Functions Multiple Stop Criteria Cross-Validation Normalize Inputs Advanced Learning Alg. Other Cost functions Automatic Model Selection Network Visual Parameter Summary Clementine Darwin Enterprise Miner Intelligent Miner Model 1 PRW NeuroShell OLPARS 1998 Elder Research T8-32
33 Enterprise Miner: Neural Network Training 1998 Elder Research T8-33
34 PRW: Neural Network Training 1998 Elder Research T8-34
35 Decision Trees "CART" C5 or C4.5 CHAID Other Priors Classification Costs Missing Data Pruning Severity Visual Trees Clementine Darwin Enterprise Miner + GainSmarts Intelligent Miner MineSet Model 1 ModelQuest CART + Scenario S-Plus See Elder Research T8-35
36 CART: Tree Browsing 1998 Elder Research T8-36
37 Scenario: Tree Browsing 1998 Elder Research T8-37
38 Enterprise Miner: Tree Browsing 1998 Elder Research T8-38
39 Enterprise Miner: Tree Results 1998 Elder Research T8-39
40 MineSet: Tree Browser 1998 Elder Research T8-40
41 Regression / Stats Linear Logistic Clementine Complexity Penalty Cross- Validation Input Selection Factor Analysis Clementine Y Enterprise Miner + + GainSmarts + + Intelligent Miner MineSet Model 1 + ModelQuest Enterprise PRW S-Plus + + S-Plus + + Scenario 1998 Elder Research T8-41
42 MineSet: Bayes Distributions 1998 Elder Research T8-42
43 Enterprise Miner: Clustering Results 1998 Elder Research T8-43
44 Intelligent Miner: Clustering Results 1998 Elder Research T8-44
45 Intelligent Miner: Cluster Explode 1998 Elder Research T8-45
46 Usability Data Loading and Manipulation Model Building Model Understanding Technical Support Overall Clementine Darwin + DataCruncher + + Enterprise Miner GainSmarts + Intelligent Miner MineSet Model ModelQuest Enterprise PRW CART Scenario NeuroShell OLPARS See5 S-Plus + WizWhy Elder Research T8-46
47 Intelligent Miner: Statistics Report 1998 Elder Research T8-47
48 Scenario: Result Reporting 1998 Elder Research T8-48
49 WizWhy: Reporting 1998 Elder Research T8-49
50 DataCruncher: Output Sensitivities 1998 Elder Research T8-50
51 Darwin: Predictions 1998 Elder Research T8-51
52 Darwin: Lift Chart (Excel) 1998 Elder Research T8-52
53 CART: Gains Chart 1998 Elder Research T8-53
54 Visualization Histograms Pie Charts Scatter/ Line Plots Rotating Scatter Conditional Plots Classification Decision Regions Correlation Plots Clementine Darwin DataCruncher Enterprise Miner GainSmarts Intelligent Miner MineSet Model 1 ModelQuest Enterprise PRW CART Scenario NeuroShell OLPARS See5 S-Plus WizWhy 1998 Elder Research T8-54
55 Clementine: Visualization User-created sub-region 1998 Elder Research T8-55
56 OLPARS: Visualization 1998 Elder Research T8-56
57 OLPARS: Decision Space 1998 Elder Research T8-57
58 Model 1: Target Sensitivities 1998 Elder Research T8-58
59 MineSet: Geographical Visualization 1998 Elder Research T8-59
60 Automation Method of Automation Free Text Annotation of Steps Clementine Visual Programming, Programming Language Darwin Programming Language DataCruncher (Task manager) Enterprise Miner Visual Programming, Programming Language GainSmarts Macro Language, Wizards Intelligent Miner (Wizards) MineSet Data History, Log Model 1 Model Wizard ModelQuest Batch Agenda PRW Experiement Manager; Macros CART Scenario NeuroShell OLPARS See5 S-Plus WizWhy Built-in Basic Scripting Scripting (S); C/C Elder Research T8-60
61 PRW: Experiment Manager 1998 Elder Research T8-61
62 Model 1: Model Summary 1998 Elder Research T8-62
63 A Recent Breakthrough: Bundling 1) Construct varied models, and 2) Combine their estimates Generate component models by varying: Case Weights Data Values Guiding Parameters Variable Subsets Combine estimates using: Estimator Weights Voting Advisor Perceptrons Partitions of Design Space 1998 Elder Research T8-63
64 Example Bundling Techniques Bayes: sum estimates of possible models, weighted by priors GMDH (Ivakhenko 68) -- multiple layers of quadratic polynomials, using two inputs each, fit by LR Stacking (Wolpert 92) -- train a 2nd-level (LR) model using leave-1-out estimates of 1st-level (neural net) models Bagging (Breiman 96) (bootstrap aggregating) -- bootstrap data (to build trees mostly); take majority vote or average Bumping (Tibshirani 97) -- bootstrap, select single best Boosting (Freund & Shapire 96) -- weight error cases by βτ = (1-e(t))/e(t), iteratively re-model; weight model t by ln(βτ) Crumpling (Anderson & Elder 98) -- average cross-validations Born-Again (Breiman 98) -- invent new X data Elder Research T8-64
65 Distinctives Strengths Weaknesses Clementine visual interface; algorithm breadth scalability Darwin efficient client-server; intuitive interface options no unsupervised; limited visualization DataCruncher ease of use single algorithm Enterprise Miner depth of algorithms; visual interface harder to use; new product issues GainSmarts data transformations, built on SAS; algorithm option depth no unsupervised; limited visualization Intelligent Miner algorithm breadth; graphical tree/cluster output few algorithm options; no automation MineSet data visualization few algorithms; no model export Model 1 ease of use; automated model discovery really a vertical tool ModelQuest breadth of algorithms some non-intuitive interface options PRW extensive algorithms; automated model selection limited visualization CART depth of tree options difficult file I/O; limited visualization Scenario ease of use narrow analysis path NeuroShell multiple neural network architectures unorthodox interface; only neural networks OLPARS multiple statistical algorithms; class-based visualization dated interface; difficult file I/O See5 depth of tree options limited visualization; few data options S-Plus depth of algorithms; visualization; programable/extendable limited inductive methods; steep learning curve WizWhy ease of use; ease of model understanding limited visualization 1998 Elder Research T8-65
66 Closing Observations Data Mining Tools Can: Enhance inference process Speed up design cycle Data Mining Tools Can Not: Substitute for statistical and domain expertise Users are advised to: Get training on tools Be alert for product upgrades 1998 Elder Research T8-66
67 Index to Tools (Page numbers in italics refer to screen captures) Clementine 7, 8, 10, 18, 20, 31, 32, 35, 41, 46, 54, 55, 60, 65 Darwin 7, 8, 10, 20, 31, 32, 35, 46, 51, 52, 54, 60, 65 DataCruncher 7, 8, 10, 19, 20, 31, 46, 50, 54, 60, 65 Enterprise Miner 7, 8, 10, 17, 20, 31, 32, 33, 35, 38, 39, 41, 43, 46, 54, 60, 65 Gainsmarts 7, 8, 10, 20, 31, 35, 41, 46, 54, 60, 65 Intelligent Miner 7, 8, 10, 16, 20, 31, 32, 35, 41, 44, 45, 46, 47, 54, 60, 65 MineSet 7, 8, 10, 14, 20, 31, 35, 40, 41, 42, 46, 54, 59, 60, 65 Model1 7, 8, 10, 20, 22, 23, 31, 32, 35, 41, 46, 54, 58, 60, 62, 65 ModelQuest 7, 8, 10, 20, 31, 35, 41, 46, 54, 60, 65 PRW 7, 8, 10, 15, 20, 21, 31, 32, 34, 41, 46, 54, 60, 61, 65 CART 7, 8, 10, 20, 31, 35, 36, 46, 53, 54, 60, 65 Neuroshell 7, 8, 10, 20, 31, 32, 46, 54, 60, 65 pcolpars 7, 8, 10, 13, 20, 31, 32, 46, 54, 56, 57, 60, 65 Scenario 7, 8, 10, 20, 24, 31, 35, 37, 41, 46, 48, 54, 60, 65 See5 7, 8, 10, 20, 31, 35, 46, 54, 60, 65 S-Plus 7, 8, 10, 20, 31, 35, 41, 46, 54, 60, 65 WizWhy 7, 8, 10, 20, 31, 46, 49, 54, 60, Elder Research T8-67
68 Forthcoming Report Report provides detailed comparison of high-end data mining tools, including capabilities, ease of use, and practical tips. Available for $695 from Elder Research ( Q Purchasers receive brief free consulting session to explore report findings in more detail, if desired. Note: The analyses and reviews were performed completely independently, and were made possible by the cooperation of the vendors, for which Elder Research is very grateful. The companies, however, provided no financial support, and had no influence on its editorial content Elder Research T8-68
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