| Intelligent Miner Help: Index |
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Navigate through the index by clicking on the letters below:
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| Where to find information about the Intelligent Miner | |
Online Help System
The Intelligent Miner online help contains detailed information on
the functions, parameters, settings, and input and output data used in
the Intelligent Miner. The help system provides example and sample data
for each function or feature. Information on how to perform a specific
task in the Intelligent Miner is included in the TaskGuide windows on the
products. These windows lead you through tasks step by step, providing
instructions for using the TaskGuide on each page. When you need more information,
click on the Help button and the online help window for the task will open.
The online help supplies reference information that will help you understand
the concept associated with the task and how your data can be used in the
task.
There are two types of help pages for most pages of a TaskGuide: Concepts
and examples and Valid values.
| Concepts and examples | Shown with titles in a yellow background and denoted
by the yellow button |
Concepts and examples pages provide discussion on theoretical and abstract concepts and explain how they relate to the functions of the Intelligent Miner and your data. These pages provide sample scenarios of how data can be used. They include example data and hints and tips.
| Valid values and actions | Shown with titles in a green background and denoted by
the green button |
Valid values and actions pages specify what type of data you can use with the function or feature. It supplies a grid that lists several fields of the Intelligent Miner GUI and describes the type of data appropriate for each field.
Printed Book: Using the Intelligent Miner for data
This book, which is also supplied online, will give you information on installing and running intelligent miner and introduce you to its features. It describes the graphical user interface (GUI) in detail. The user's manual is an excellent source of information when you begin using the product, and as a reference for specific information about parameters, functions, concepts, and values. The book will also guide you through a simple scenario using the Intelligent Miner. It also covers interpereting the results of the data after a mining run. The book also contains a glossary of terms that are commonly associated with data mining and those specific to the Intelligent Miner.
Also included with the Intelligent Miner is an HTML version of the user's manual. This is simply an electronic format for the paper book.
Return to table of Contents
Go to Index
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| Notices | |
References in this publication to IBM products, programs, or services do not imply that IBM intends to make these available in all countries in which IBM operates. Any reference to an IBM product, program, or service is not intended to state or imply that only IBM's product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any of IBM's intellectual property rights may be used instead of the IBM product, program, or service. Evaluation and verification of operation in conjunction with other products, except those expressly designated by IBM, is the user's responsibility.Licensees of this program who wish to have information about it for the purpose of enabling; (i) the exchange of information between independently created programs and other programs (including this one) and (ii) the mutual use of the information which has been exchanged, should contact IBM Deutschland Informationssysteme GmbH, Department 3982, Pascalstrasse 100, 70569 Stuttgart, Germany. Such information may be available, subject to appropriate terms and conditions, including in some cases, payment of a fee.
IBM may have patents or pending patent applications covering subject matter in this document. The furnishing of this document does not give you any license to these patents. You can send license inquiries, in writing, to the IBM Director of Licensing, IBM Corporation, 500 Columbus Avenue, Thornwood NY 10594, U.S.A.
The Intelligent Miner incorporates code generated by the Purdue Compiler Construction Tool Set (PCCTS).
Trademarks and service marks
The following terms are trademarks of the IBM Corporation in the United States, or other countries, or both:Licensed Materials - Property of IBMJava and all Java-based trademarks and logos are trademarks of Sun Microsystems, Inc. in the United States and other countries.
ACF/VTAM
AIX
AS/400
Common User Access
DATABASE 2
DB2
DB2 Universal Database
DFSMS
Distributed Relational Database Architecture
DRDA
IBM
Language EnvironmentMVS
MVS/ESA
OS/2
OS/390
OS/400
Parallel Sysplex
Power PC
RACF
RS/6000
SAA
SP2
System/390Microsoft, Windows, and Windows NT are registered trademarks of Microsoft Corporation.
UNIX is a registered trademark in the United States and other countries licensed exclusively through X/Open Company Limited.
Other company, product, and service names may be trademarks or service marks of others.
© Copyright IBM Corp. 1996, 1998 All Rights
Reserved
US Government Users Restricted Rights - Use, duplication or disclosure
restricted by GSA ADP Schedule Contract with IBM Corp.
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Create settings from output data |
Key:
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Concepts and examples |
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Valid values and actions |
Help contents
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TaskGuide Table of Contents
About the Intelligent Miner
Where to find information about the Intelligent MinerCreating and defining data objects:
Using the main window
Using the preferences notebook
- Database table
- Database view
- Flat files
- Using existing data
- Mapping names manually
- Using existing data
- Mapping data manually
- Using existing data
- Manually creating taxonomy relationship
- Creating discretization using existing data
- Manually create a discretization
- Connecting to data
- Mapping data names
- Mapping data values
- Creating taxonomy (classification) relationships
- Separating data into categories (discretization)
Aggregating valuesMining your data
Calculating values
Cleaning up data sources (deleting data sources)
Converting uppercase to lowercase (or vice versa)
Copying records to a file
Discarding records with missing values
Discretizing into quantiles
Discretizing using ranges
Encoding missing values
Encoding nonvalid values
Filtering fields
Filtering records
Filtering records using a value set
Getting a random sample
Grouping records
Joining data sources
Mapping values
Pivoting fields to records
Running SQL
- Discovering basic associations between data
- Discovering advanced associations between data
- Discovering basic demographic clusters
- Discovering advanced demographic clusters
- Using basic neural networks
- Using advanced neural networks
- Discovering basic sequential patterns
- Discovering advanced sequential patterns
- Discovering basic similar time sequences
- Discovering advanced time sequences
- Using basic neural networks
- Using advanced neural networks
- Predicting basic classifications using tree-induction
- Predicting advanced classifications using tree-induction
- Using basic neural networks
- Using advanced neural networks to predict values
- Using basics of the Radial Basis Function (RBF)
- Advanced use of the RBF
- Discovering associations
- Discovering clusters
- Discovering sequential patterns
- Discovering similar time sequences
- Predicting classifications
- Predicting values
- Basic bivariate statistics
- Advanced bivariate statistics
- Basic correlation matrices
- Advanced correlation matrices
- Basic cross-correlation
- Advanced cross-correlation statistics
- Basic factor analysis
- Advanced factor analysis
- Basic linear regression
- Advanced linear regression
- Basic principal component analysis
- Advanced principal component analysis
- Basic univariative curve fitting
- Advanced univariative curve fitting
- Bivariate statistics
- Correlation matrices
- Cross-correlation
- Factor analysis
- Linear regression
- Principal component analysis
- Univariate curve fitting
Creating a sequence
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