1. Mining data with PolyAnalyst Your Knowledge Partner TM

2. Outline Data Mining in BI chain PolyAnalyst overview
Learning algorithms
Additional features
Future developments

3. Your Knowledge Partner TM
Data Mining
in BI chain
Your Knowledge Partner TM

4. DM in Decision Making Consider a fragment of the BI chain: Data
Knowledge
Decision
Action
Data - is what we can capture and store
Knowledge - is what provides for informed decisions
Problem: How to get from Data to Knowledge?
Solution: Data Mining (Machine Learning)

5. Data Mining
"Data Mining is the process of identifying valid, novel, potentially useful, and ultimately comprehensible knowledge from databases that is used to make crucial business decisions."
-- G. Piatetsky-Shapiro, KDNuggets editor
Valid
Novel
Actionable
Comprehensible

6. Data Mining vs. OLAP
OLAP - Helps prove or reject your hypotheses by dissecting data along different dimensions - But you have to guess the answer first !
Data Mining - Automatically develops and tests numerous hypotheses by learning from historical data - Analyzes raw data

7. Business Intelligence Chain
Consider direct marketing automation
Analyze data
Integrate applications X

8. Data Mining Tasks Predicting Classifying Clustering Segmenting
Explaining
Associating
Visualizing
Link Analysis
Text Mining

9. Fields of application

10. What makes DM hard? Unfamiliar concept and lack of experience
Results require interpretation by an analyst
Poor integration in existing applications
Difficulty processing very large databases
Necessity to learn a new application
High cost

11. Megaputer response
Challenge: Unfamiliar concept and lack of experience Response: Collaborative Appliance Program – combines Megaputer analysts expertise in data mining and customer knowledge of the business project
Challenge: Results require interpretation by an analyst Response: Simple reporting and batch processing capabilities
Challenge: Poor integration in existing applications Response: Easy scoring of external data with a few mouse clicks
Challenge: Difficulty processing very large databases Response: In-Place Data Mining
Challenge: Necessity to learn a new application Response: An SDK of easy-to-integrate PolyAnalyst COM components
Challenge: High cost Response: Flexible licensing mechanism

12. Your Knowledge Partner TM
PolyAnalyst
overview
Your Knowledge Partner TM

13. What is PolyAnalyst? Multi-strategy data mining suite Ease-of-use:
The largest selection of ML algorithms for diverse business tasks
Structured data and text processing tools
Ease-of-use:
friendly data manipulation and visualization
Deep integration
Applying models to external DB through the OLE DB protocol
Exporting models to XML
COM components
Best Price/Performance ratio

14. Key differentiators of PolyAnalyst
Integrated analysis of structured (numeric and categorical) and unstructured (text) data
Easy to learn and operate visual analytical interface
The largest selection of powerful machine learning algorithms
Mouse-driven application of predictive models to data in any external system through a standard OLEDB link
Simple integration with external applications: SDK of COM components
In-Place Data Mining capabilities for processing huge databases
Step-by-step tutorials based on real-world case studies
Rich data manipulation and visualization tools
Reusable analytical scripts for batch process data mining
The best Price/Performance ratio

15. Customer base: 300+ installations
PolyAnalyst
Customer base: 300+ installations
Sample customers
Boeing (USA)
3M (USA)
Chase Manhattan Bank (USA)
McKinsey & Company (USA)
Siemens (Germany)
Lockheed Martin (USA)
Allstate Insurance (USA)
ICICI Bank (India)
Mars (USA)
Taco Bell (USA)
DuPont (USA)
Asea Skandia (Sweden)
France Telecom (France)
Cambridge Technology Partners (USA)
Carlson Marketing (USA)
Central Bank (Russia)
US Navy (USA)
KPN Research (Netherlands)
Alka Insurance (Denmark)
National Cancer Institute (USA)

16. PolyAnalyst workplace
Control buttons
Project
navigation tree
Data and Results
pane
Objects and Collections
represented by icons
Exploration engine
report fragment
PolyAnalyst
log journal

17. PolyAnalyst provides
Access to data held in a database or data warehouse
Numerical
Categorical
Yes/no
Date
Data manipulation and visualization
14 machine learning algorithms
Convenient results reporting and outputing
Integration with external applications

18. Your Knowledge Partner TM
PolyAnalyst
machine learning algorithms
Your Knowledge Partner TM

19. “Probably one the most impressive characteristic of PolyAnalyst is the sheer number of data mining tasks it can tackle.”
Mario Apicella
Technology Analyst
InfoWorld Test Center
July 3, 2000

20. Learning algorithms Find Laws (SKAT algorithm)
Cluster (Localization of anomalies)
Find Dependencies (n-dimensional distributions)
Classify (Fuzzy logic modeling)
Decision Tree (Information Gain criterion)
PolyNet Predictor (GMDH-Neural Net hybrid)
Market Basket Analysis (Association rules)
Memory Based Reasoning (k-NN + GA)
Linear Regression (Stepwise and rule-enriched)
Discriminate (Unsupervised classification)
Summary Statistics (Data summarization)
Link Analysis (Visual correlation analysis)
Text Mining (Semantic text analysis)

21. Cluster (FC) Identifies clusters of similar records
Selects best variables for clustering
Suggests the number of clusters
Separates clusters of records in new data sets for further investigation - preprocessing for other algorithms

22. Groups of similar records
Cluster (continued)
Groups of similar records

23. Cluster (continued)
Based on analyzing distributions in hypercubes of all variables rather than on measuring distances between points
Hence, independent of rescaling of axes variable
Finds only clusters actually present in data, on the background of uniformly distributed cases

24. Classify (CL) Fuzzy-logic based classification
The function of belonging modeled by either Find Laws, PolyNet Predictor, or LR
Provides record scoring with Lift and Gain charts used for visualization
Assigns records to one of two classes and furnishes utilized classification rule

25. Classify (continued)
Mass mailing
Targeted mailing
PolyAnalyst Lift chart illustrates an increase in the response to a campaign based on the discovered model - instead of random mailing
% of maximal possible response
Mass mailing
Targeted mailing
Profit ($)
PolyAnalyst Gain chart helps optimize the profit obtained in a direct marketing campaign

26. Decision Tree (DT) Intuitively classifies cases to selected categories
Based on Information Gain splitting criteria
The fastest algorithm in PolyAnalyst
Scales linearly with increasing number of records

27. Decision Tree (continued)
Node characteristics
Classification tree

28. Decision Forest (DF)
The most efficient classification algorithm for tasks with multiple target categories
Transforms the task of categorizing data records to N classes into the problem of solving N tasks of categorizing records to two classes
Develops the best collection of N classification trees, with leaves containing probabilities of classifying records in the corresponding classes
Scales linearly with increasing number of records

29. Link Analysis (LK) Reveals pairs of correlated objects
Used in Fraud Detection, Text Analysis and other correlation analysis tasks

30. Text Analysis (TA) Extracts key concepts from natural language notes
Tags individual records with the main encountered concepts
Recognizes synonyms and othe semantic relations
Can perform user-focused or unsupervised analysis
Integrates the analysis of text with the power of other machine learning algorithms of PolyAnalyst
Facilitates categorization of textual documents

31. Text Analysis (continued)

32. Basket Analysis (BA)
Is used in Retailing, Fraud Detection and Medicine
Identifies in transactional data groups of products sold together well
Finds directed association rules for each of these groups
Groups baskets containing similar sets of products
Characterized by
Support
Confidence
Improvement
Based on new mathematics:
works 10 to 50 times faster than traditional algorithms

33. Basket Analysis (continued)
Groups of products
sold together well
Directed
Association Rules

34. Basket Analysis (continued)
Works with both transactional and flat data format
Easily finds many-to-one rules
“I would like to continue working together with Megaputer on other CTP customers’ projects (mainly Swedish and Danish Banks ).”
-- Olof Goransson
Senior Data Consultant
CTP Skandinavien AB

35. Find Laws (FL) Models relationships hidden in data
Presents discovered knowledge explicitly
Searches the space of all possible hypotheses
“The unique Find Laws algorithm along with an easy to use interface made PolyAnalyst the only choice for our environment.”
-- James Farkas, Senior Navigation Engineer, The Boeing Company

36. Find Laws (continued) FL is based on the Megaputer’s unique
Symbolic Knowledge Acquisition Technology (SKAT)
A good introduction to SKAT: PCAI magazine, January 99, p

37. Find Dependencies (FD)
Determines most influential variables
Detects multi-dimensional dependencies
Predicts target variable in a table format
Used as preprocessing for FL

38. Find Dependencies (continued)
Predicted Sales per Employee

39. PolyNet Predictor (PN)
Predicts values of continuous attributes
Hybrid GMDH-Neural Network method
Works well with large amounts of data
The best architecture network is built automatically

40. Memory Based Reasoning (MB)
Performs classification to multiple categories
Based on identifying similar cases in the previous history
Uses Genetic Algorithms to find the most suitable metric for the problem

41. Discriminate (DS)
Determines what features of a selected data set distinguish it from the rest of the data
Requires no target variable
Can be powered by
Find Laws
PolyNet Predictor
Linear Regression

42. Linear Regression (LR)
Incorporates categorical and yes/no variables in the analysis correctly
Stepwise Linear Regression: only influential variables included
Can be used as a preprocessing and benchmarking module

43. Your Knowledge Partner TM
PolyAnalyst
features in more detail
Your Knowledge Partner TM

44. Data Analysis Project Workflow
Access data
Understand, clean and transform data
Run machine learning analysis
Visualize, report and share results
Integrate results in existing business process

45. Data Access ODBC-compliant databases: Dedicated access
Oracle, DB2, Informix, Sybase, MS SQL Server, etc.
Dedicated access
IBM Visual Warehouse
Oracle Express
OLE DB (can do In-Place Data Mining)
CSV or DBF files
Data can be appended to the project when necessary

46. Data cleansing and manipulation
SQL querying through OLE DB
Records selection according to multiple criteria
Union, intersection, or complement of data sets
Categorical values aggregation
Visual Drill-through
Exceptional records filtering
Split into n-tile percentage intervals
Random sampling

47. Visualization Histograms
Line and scatter plots with zoom and drill-through capabilities
Snake charts
Interactive 3D-charts
Interactive Rule-graphs with sliders for visualizing multi-variable relations
Frequency charts for categorical, integer, or yes/no variables
Lift and Gain charts for marketing applications

48. Histograms and Frequencies
Histogram displays distribution of numerical variables
Frequencies chart displays distribution of categorical and yes/no variables

49. 2D charts and Rule-graphs
Sliders help visualize effects of other variables in more than two-dimensional models
The Find Laws model (red line) for a product market share dependence on the price predicts a dramatic change in the formula when the product goes on promotion

50. Snake-charts
Quickly compare qualitatively several datasets on all their attributes
“High”
Compared data sets
All variables
“Low”

51. Interactive 3D charts
You can use mouse to rotate the 3D-cube

52. Your Knowledge Partner TM
PolyAnalyst
integration features
Your Knowledge Partner TM

53. Integration objectives
Use models to simply score data in various external databases
Deliver models to external applications in the format they understand - XML
Be able to analyze very large databases in their entirety
Integrate dedicated machine learning components in existing decision support systems

54. Applying models externally
PolyAnalyst can readily apply predictive models directly to data in any external source through a standard OLE DB protocol
PolyAnalyst can export models to XML (PMML) format for their incorporation in external decision support applications

55. Analyzing large databases
Traditional Data Mining
In-Place Data Mining

56. PolyAnalyst COM A kit of COM-based Data Mining components Benefits
See DMReview magazine, January 2000, p. 42 and PCAI magazine, March 99, p. 16
Benefits
Develop new applications quickly and effortlessly
Incorporate third party components
Choose best components from different vendors
Extend functionality by adding new components
Cross-platform applications
Integration with most simple tools (Visual Basic)

57. PolyAnalyst COM (continued)
Offers individual machine learning engines
Integration with external applications
Hard analytical work is performed by integrated PolyAnalyst machine learning components behind the scenes
The main program instructs PolyAnalyst on how to access the stored data
Users see only the familiar interface enhanced by a few new buttons

58. PolyAnalyst platforms
Standalone system:
PolyAnalyst - Windows 9x/NT/2000/XP
PolyAnalyst Pro - Windows NT/2000P/XP Pro
PolyAnalyst XL - Add-ins for MS Excel
Client/Server system:
PolyAnalyst Knowledge Server - Windows NT
Client - Windows 9x/NT/2000 or OS/2

59. Your Knowledge Partner TM
Customer quotes
Your Knowledge Partner TM

60. PolyAnalyst supports medical projects at 3M
“Analytical engines do an excellent job of finding relations amongst many fields without overfitting.”
Timothy Nagle Consulting Scientist 3M Corporation St. Paul, MN, USA

61. PolyAnalyst helps improving flight control system at Boeing
“PolyAnalyst provides quick and easy access for inexperienced users to powerful modeling tools.
James Farkas Senior Navigation Engineer The Boeing Company Kent, WA, USA

62. PolyAnalyst facilitates marketing research at Indiana University
Raymond Burke E.W. Kelley Professor of BA Kelley Business School Indiana University Bloomington, IN, USA
“PolyAnalyst provides a unique and powerful set of tools for data mining applications, including promotion response analysis, customer segmentation and profiling, and cross-selling analysis.”

63. PolyAnalyst helps medical research at the University of Wisconsin-Madison
Prof. Roger L. Brown Director of RDSU University of Wisconsin Madison, WI, USA
“PolyAnalyst suite enabled our researchers to search their data for rules and structure while providing a symbolic knowledge of the structure, the detail they needed.”

64. PolyAnalyst provides efficient machine learning algorithms
Mario Apicella Technology Analyst InfoWorld Test Center
“PolyAnalyst focuses more effectively on data discovery than its competition.”

65. Your Knowledge Partner TM
PolyAnalyst
future developments
Your Knowledge Partner TM

66. Future developments Further support for OLE DB for DM
Nested tables
New machine learning algorithms
Time series analysis
Kohonen maps
Enhanced data import and manipulation
Visual development of workflow scripts
New push-button vertical applications

67. PolyAnalyst -- WebAnalyst
PolyAnalyst supports support visual project development when used on top of a new Megaputer web-enabled enterprise server, WebAnalyst

68. PolyAnalyst evaluation
Download a FREE evaluation copy of PolyAnalyst from
and enjoy using it hands-on following the provided step-by-step lessons, or exploring your own data.

69. Your Knowledge Partner TM
Any Questions?
Call Megaputer at
(812)
or write
120 W Seventh Street, Suite 310
Bloomington, IN USA
Your Knowledge Partner TM

70. Case 1: Asea Skandia (Sweden)

71. Asea Skandia Established 1907
Largest Swedish distributor of electrical equipment
About 1,400 employees and a turnover of SEK 5.1 billion
About ten thousand product names
Not good at CRM and DB marketing yet
Had only transactional data in a database

72. Groups of products offered
Home Appliances
90 Cookers, cooker fans, microwave ovens
91 Fridges/Chillers/Freezers
92 Washing machines, dishwashers, dryers
93 Sauna unit, fans
94 Small appliances
Lightning
17 IR, RF and Bus control systems
19 Light reg.. timers, plugs, CCE-con., car heaters
70 Interior light fittings
72 Industrial light fittings
73 Emergency luminaires
74 Spotlights and downlights, lighting tracks
75 Decorative interior light fittings
77 Exterior light fittings
79 Accessories and spare parts
80 Fluorescent lamps and other discharge lamps
81 Incandescent filament and halogen lamps
82 Special lamps
Ventilation and sheet metal
15 Fastening and fixings, protective equipment
16 Tools, implements, protective equipment & clothin
66 Ventilation
67 Sheet Metal for Buildings
 Telecommunications
48 Low current cable
49 Data and optical fiber cable
50 Network material
51 Local data networks
52 Power Supply
53 Signalling equipment
55 Distress signal systems
57 Telephony
58 Internal communication systems
60 Aerial equipment
62 Sound and time distribution systems
63 Safety and Security Systems
64 Service Alarm Systems
Electrical Equipment
1 Power and control cables
2 Electrical installation, wiring and flexible cable
6 Material kits, cable protection, lightning equipment
7 Terminations, joints, cabinets and electrical tape
8 Contact crimping
9 Electric meters
11 Cable ladders, trays, trunking, cable trolleys
14 Conduit, boxes, glands, fire protection
18 Switch systems
20 Fuses with accessories
21 Miniature circuit breaker systems
22 Distribution board systems IP20-IP43
23 Distribution board systems IP43-IP65
25 Equipment boxes, equipment cabinets
26 Distribution board accessories
28 Switchgear components, capacitors, busbar trunking
29 Connection terminals and marking materials
31 Motor, safety, load and MCCB breakers
32 Contactors and starters
35 Motors
37 Push switches
38 Sensors, monitors and regulators
40 Relays, time relays
42 Metering instruments
43 Spare parts for consumer goods
45 Programmable control system
85 Radiators and thermostats
87 Fan heaters
88 Water heaters and electric boilers
89 Heating cable

73. Asea Skandia CTP Megaputer
(continued)
Predicting cross-sell opportunities was possible
Closer cooperation with the client was necessary
Megaputer teamed with Cambridge Technology Partners (Sweden)
Data was disguised prior to the analysis
Asea Skandia
CTP
Megaputer
Identified new opportunity
Hired a consultant
Helped aggregating products in groups
Incorporated results in marketing activities
Identified most suitable solution provider
Worked with the client
Collected available data
Aggregated data in product categories
Presented Megaputer results to the client
Determined business potential of the data
Developed data exploration strategy
Carried out Market Basket Analysis
Provided actionable results to CTP

74. PolyAnalyst MBA
Works times faster than traditional
Easily finds many-to-one rules
“I would like to continue working together with Megaputer on other CTP customers’ projects (mainly Swedish and Danish Banks ).”
-- Olof Goransson
Senior Data Consultant
CTP Skandinavien AB