megaputer intelligence 2000. 3. 27 인공지능연구실 석사 2 학기 최윤정...
TRANSCRIPT
Outline
Overview
Technology
PolyAnalyst solution overview
Customer cases
Future developments
Megaputers…
Overview
1989 년 모스크바 주립대학 AI 연구소
Knowledge Discovery Semantic 정보검색 및 분석에 기반을 둠 .
1994 년Polyanalyst1.0 개발
2000
Technology Subject-Oriented analytical systems
Statistical packages
Neural Networks
Evolutionary Programming
Memory Based Reasoning(MBR)
Decision Tress
Genetic Algorithms
데이타마이닝과 지식탐사를 위한 툴과 semantic
Text 분석 , information retrieval 을
위한 툴 제공PolyAnalyst 4.0
PolyAnalyst COM
TextAnalyst 1.5
TextAnalyst Com
MegaSearch tm
Product
PolyAnalystoverview
Features in more detail
multi-strategy data mining suite
utilizing the latest achievements in knowledge discovery
with a broad selection of exploration engines
powerful data manipulation and visualization tools
Modeling
Predicting
Clustering
Classifying
Explaining
PolyAnalyst workplaceMultiple machine learning algorithms can be accessed through pull-down and pop-up menus, or control buttons
The project data, charts, discovered rules, and system reports are represented by icons held in separate containers
Learning algorithms
Find Dependencies
PolyNet Predictor
Cluster
Find Laws
Classify
Dis
crim
inat
e
Line
ar R
egre
ssio
n
Identifies a set of the most influential predictors and determines outliers
Predicts values of the target variable - a hybrid of GMDH and Neural Net algorithms
Separates groups of similar records and finds the best clustering variables
Finds an explicit model for the relation predicting the target variable
Assigns cases to two different classes by utilizing Fuzzy Logic
Determines what characteristics of a specified data set distinguish it from the rest of the data
Stepwise linear regression - correctly treats categorical and yes/no variables
New algorithm: robustly classifies records into multiple categories
Memory Based Reasoning
Pol
yAna
lyst
CO
M
Find Dependencies
Outliers
Most influential variables
determined
Predicted target value for a cell
All considered variables
ClusterVariables providing the best clustering
Individual clusters
Cluster sequential number
Number of points in a cluster
PolyNet Predictor
PolyNet PredictorR^2 = 0.93
Linear RegressionR^2 = 0.86
Similar to all other PolyAnalyst algorithms the best PN model is found as an optimal solution in terms of
The following graphs display the accuracy of PN and LR models developed to predict relative performance of computers from different manufacturers:
Pre
dic
ted
vs.
Act
ual t
arge
t va
riabl
e
Classify
Mass mailing
Targeted mailingPolyAnalyst Lift chart illustrates an increase in the response to a campaign based on the discovered model - instead of random mailing
% o
f m
axim
a l
poss
ible
re s
pons
e
Mass mailing
Targeted mailingPr
ofit
($)PolyAnalyst Gain chart helps optimize the profit obtained in a direct marketing campaign
Linear Regression
Yes/no variable taken into account correctly
Partial contributions of individual terms in the linear regression formula
Discriminate algorithm
Determines what features of a selected data set distinguish it from the rest of the data
Requires no preset target variable Can be powered by
Find Laws PolyNet Predictor Linear Regression
Memory-Based ReasoningPerforms classification to multiple categoriesIs based on identifying similar cases in the previous historyImplemented only in PolyAnalyst COM (available in the end of March 1999)
Data Access PolyAnalyst works with ODBC-compliant
databases: Oracle, DB2, Informix, Sybase, MS SQL Server, etc.
A customized version works with IBM Visual Warehouse Solution and Oracle Express
Data and exploration results can be exchanged with MS Excel
CSV or DBF format files
New data can be added to the project when necessary
Visualization
Data can be displayed in various visual formats:
Histograms
Line and point plots with zoom and drill-through capabilities
Colored charts for three dimensions
Interactive rule-graphs with sliders help visualizing and manipulating multi-variable relations
Frequencies charts provide for a quick and thorough visualization of the distribution of categorical, integer, or yes/no variables
Lift and Gain charts are very useful in marketing applications
Histograms and Frequencies
Histogram displays distribution of numerical variables
Frequencies chart displays distribution of categorical and yes/no variables
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
PolyAnalyst platforms
Standalone system:
PolyAnalyst Power - Windows 95/98/NT PolyAnalyst Pro - Windows NT PolyAnalyst Lite - Windows 95/98/NT PolyAnalyst 2.1 - IBM OS/2
Client/Server system:
PolyAnalyst Knowledge Server - Windows NT or OS/2
Client - Windows NT, 95, 98, or OS/2
Sample customer cases
PolyAnalyst supports medical projects at 3M
Timothy NagleConsulting Scientist3M CorporationSt. Paul, MN, USA
“Analytical engines do an excellent job of finding relations amongst many fields without overfitting. I found the user interface both intuitive and easy to use.
Megaputer support is outstanding. The inevitable problems one expects with a complex system are dealt with immediately.”
PolyAnalyst helps improving flight control system at Boeing
James FarkasSenior Navigation EngineerThe Boeing CompanyKent, WA, USA
“PolyAnalyst provides quick and easy access for inexperienced users to powerful modeling tools. The user interface is intuitive and new users come up to speed very quickly. Interfaces to spreadsheet tools provide flexibility needed to work solutions as a team.”
PolyAnalyst facilitates marketing research at Indiana University
Raymond Burke E.W. Kelley Professor of BA Kelley Business School Indiana UniversityBloomington, 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.
Unlike neural network programs, PolyAnalyst displays a symbolic representation of the relationship between the independent and dependent variables - a critical advantage for business applications.”
PolyAnalyst helps medical research at the University of Wisconsin-Madison
Prof. Roger L. BrownDirector of RDSUUniversity of WisconsinMadison, 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.
The software has provided very interesting results for one of our projects, which had been presented at a major cardiology meeting.”
PolyAnalyst enjoys international success
“PolyAnalyst scores extremely well by providing a complete environment in which almost any research worker could data mine his or her own data. It is a very useful product, potentially with a wide user base, and it appears to me to be unique.”
“PolyAnalyst proves capable of providing models for building reliable trading strategies even for a difficult to predict FOREX market. PolyAnalyst is a leader in reliability, accuracy, and diversity of automatically built models.”
Alexander Fomenko Director Analytical Dept Killiney Investments Europe Rep.Moscow, Russia
David McIlroy Analytical Department Master Foods Olen, Belgium
Product Price $$
Custom-build own PolyAnalyst system!
Product Price $$(continue)
Custom-build own PolyAnalyst system!- COM 모듈은 어플리케이션을 작성하는데 적당- 각각의 필요한 알고리즘에 해당하는 Tool Kit 을 구입할 수 있음
Future developments
New machine learning algorithms: Memory Based Reasoning Weighted variable Clustering and Classification
PolyAnalyst COM built on the basis of Component Object Model - an integrated kit for simple development of decision support applications utilizing advanced PolyAnalyst algorithms (see PCAI Magazine, March 99, p. 16-19)
Enhanced graphics (Snake and Boxplot charts) and data import and manipulation
PolyAnalyst evaluation
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