k7`5`5by nameby nameBUILDd kHA./AA k m*P5`5`5 3*./lpp_namemm4 R S IMiner.html.en_US.concepts { IMiner.html.en_US.concepts 02.01.0001.0001 01 N U en_US Intelligent Miner for Data - Examples & Concepts - US Engl. [ *prereq IMiner.html.en_US.concepts 2.1.1.0 % /usr/lpp/IMiner/html 184 /usr/lib/objrepos 8 INSTWORK 56 24 % % % IX99999 Missing help texts. % ] } k\A./usrAA k{A./usr/lppAA kyA./usr/lpp/IMiner.html.en_US.concepts/IMiner.html.en_US.concepts/2.1.1.1AA kΫ m_5_5_5 ./usr/lpp/IMiner.html.en_US.concepts/IMiner.html.en_US.concepts/2.1.1.1/liblpp.amm 5212 0 68 5084 0 270 464 0 895520655 6166 200 644 36 IMiner.html.en_US.concepts.copyright` Licensed Materials - Property of IBM 5697IM200 (C) Copyright International Business Machines Corporation 1996, 1998 All rights reserved. US Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. 3388 3978 68 901621599 6166 200 640 36 IMiner.html.en_US.concepts.inventory` /usr/lpp/IMiner/html/idmelfxb.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 2578 checksum = "49925 3 " /usr/lpp/IMiner/html/idmelmxb.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 2973 checksum = "14399 3 " /usr/lpp/IMiner/html/idmem1au.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 5143 checksum = "59715 6 " /usr/lpp/IMiner/html/idmem2b4.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 2293 checksum = "46564 3 " /usr/lpp/IMiner/html/idmem2bu.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 6783 checksum = "51034 7 " /usr/lpp/IMiner/html/idmem2du.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 5641 checksum = "39294 6 " /usr/lpp/IMiner/html/idmem3au.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 5008 checksum = "22340 5 " /usr/lpp/IMiner/html/idmem4au.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 4402 checksum = "20615 5 " /usr/lpp/IMiner/html/idmem5bu.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 6531 checksum = "47634 7 " /usr/lpp/IMiner/html/idmem5du.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 5534 checksum = "37440 6 " /usr/lpp/IMiner/html/idmem6bu.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 7062 checksum = "64158 7 " /usr/lpp/IMiner/html/idmem6cp.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 4013 checksum = "25539 4 " /usr/lpp/IMiner/html/idmem6du.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 5926 checksum = "15788 6 " /usr/lpp/IMiner/html/idmep99o.htm: owner = bin group = bin mode = 644 type = FILE class = apply,inventory,IMiner.html.en_US.concepts size = 2626 checksum = "25354 3 " 490 4588 464 901621599 6166 200 640 29 IMiner.html.en_US.concepts.al` ./usr/lpp/IMiner/html/idmelfxb.htm ./usr/lpp/IMiner/html/idmelmxb.htm ./usr/lpp/IMiner/html/idmem1au.htm ./usr/lpp/IMiner/html/idmem2b4.htm ./usr/lpp/IMiner/html/idmem2bu.htm ./usr/lpp/IMiner/html/idmem2du.htm ./usr/lpp/IMiner/html/idmem3au.htm ./usr/lpp/IMiner/html/idmem4au.htm ./usr/lpp/IMiner/html/idmem5bu.htm ./usr/lpp/IMiner/html/idmem5du.htm ./usr/lpp/IMiner/html/idmem6bu.htm ./usr/lpp/IMiner/html/idmem6cp.htm ./usr/lpp/IMiner/html/idmem6du.htm ./usr/lpp/IMiner/html/idmep99o.htm 45 4756 3978 901621599 6166 200 640 31 IMiner.html.en_US.concepts.size` /usr/lpp/IMiner/html 184 /usr/lib/objrepos 8 204 5084 4588 901620737 6166 200 750 34 IMiner.html.en_US.concepts.fixdata` fix: name = IX80728 abstract = Missing help texts. type = f filesets = "IMiner.html.en_US.concepts:2.1.1.1\n\ " symptom = "Some help texts are not available \n\ " 27 5212 4756 895520633 6166 200 644 9 productid` IMiner.clientEN 5697-IM200 265 0 5084 0 0 0 0 0 ` 6 68 464 3978 4588 4756 5084 IMiner.html.en_US.concepts.copyrightIMiner.html.en_US.concepts.inventoryIMiner.html.en_US.concepts.alIMiner.html.en_US.concepts.sizeIMiner.html.en_US.concepts.fixdataproductid k>$' ^5p5q5 a ./usr/lpp/IMiner/html/idmelfxb.htm idmelexb.htm
Computed fields expression builder: Data TaskGuide
   
Use this page to create an expression for a computed field function. You can create an expression using an item from one of the following categories:
  • Computed functions
  • Constants
  • Field names
 
To create a computed fields function:
  1. Select Computed Functions from the Category list.

    2.  
  2. Select a function from the Value list.

    3.  
  3. Select either Constants or a Field names from the Category list.

    To create a new constant, double-click on <new constant> from the Value list and enter the new constant.
     

  4. Specify the arguments for the function by first clicking on an item from the Value list and then clicking on the appropriate Arg button.

    5. Note that the type of the value might enable or disable the Arg button.

    To create new arguments, click on the ... button next to the last Arg button.
     

  5. Repeat the previous steps to create a computed field function.
 
Your expression might look like this:
 
Numeric divide (Quantity, 2)
 

 
// k,( ^5p5q5 b ./usr/lpp/IMiner/html/idmelmxb.htm idmelmxb.htm
Expression builder: Filter records
   
Use this page to create expressions to filter the records of the input data.

You can create expressions using an item from one of the following categories:

Select Constants or Field names in the Category list to display the appropriate values in the Values list.

To create the constant 1997:

The new constant is added to the list of constants in the Value list.

You can edit any clause of the form Arg1=Arg2.

To edit a clause:

You might want to use the following expression :
Customer age>50 AND Date=1997 AND Total purchase amount>100$
 

@ k*')^5p5q5 c./usr/lpp/IMiner/html/idmem1au.htm idmem1au.htm  
Summary: Associations mining function  
   
Use this page to verify the parameters you specified for the Associations mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings name Sample settings
Comment Data collected during 1st half of 1997
Mining function Associations
   
Input data Sunset June transactions
Comment Branch locations New York City
Optimize mining run for Time
Filter records condition Conditions selected
Power options  
   
Filter mask Selected
Transaction ID Customer number
Item ID Article
Filter mask sales*
Sort the input data on the values in the Transaction ID field before running this function False
   
Minimum support 5
Minimum confidence 75
Maximum rule length 3
Item constraints 5 selected
Run the parallel  mode of this function Use this number of parallel processes: 4
   
Taxonomy name Sunset non-food
Comment Different taxonomies in New York City, Washington D.C., and Cleveland
   
Results name Sunset June associations
Comment Branch locations New York City
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


e kJ*^5p5q5 e./usr/lpp/IMiner/html/idmem2b4.htm idmem2b4.htm  
Value Mapping
   
Use this page to select an existing or to specify a new value mapping for similarity definitions. You can also associate or modify a comment.

You can use value mappings to define symmetric similarities for discrete fields. The value mapping contains two values and the similarity definition. During the clustering, both values use the specified similarity.

You can specify the similarity for each pair of possible values. One pair of possible values can only be used once. The similarity value for both values is the same. The similarity value must be between 0 and 1.

 
You might want to create a new value mapping called Marital status similarity definitions: 
Settings name Comment
Marital status similarity definitions  
 

CO k+^5p5q5 f./usr/lpp/IMiner/html/idmem2bu.htm idmem2au.htm  
Summary: Demographic Clusters mining function  
   
Use this page to verify the parameters you specified for the Demographic Clusters mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings name Sample settings
Comment Data collected during 1st half of 1997
Mining function Demographic Clusters
   
Input data Quality food supermarket
Comment May transactions
Optimize mining run for Time
Filter records condition Conditions selected
Power options  
   
Use mode Clustering mode
Maximum passes 2
Maximum clusters 9
Accuracy 10
Similarity threshold 0,5
   
Active fields 6 selected
Supplementary fields 3 selected
Filter mask Customer
   
Field parameters  5 selected 
Additional field parameters 5 selected
Outlier treatment Treat outliers as missing values
Similarity matrix 1 selected
   
Run the parallel mode of the function Use this number of parallel processes: 4
Additional parallel parameters Selected
   
Output fields 1 selected
Cluster ID field name Cluster ID 
Record score field name  
Cluster ID field name choice 2  
Record score field name choice 2  
Confidence field name  
   
Output data Quality food demographic clusters
Comment  May transactions 
   
Result name Quality food demographic clustering result
Comment May transactions
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


< kp, ^5p5q5 h ./usr/lpp/IMiner/html/idmem2du.htm idmem2du.htm  
Summary: Neural Clusters mining function  
   
Use this page to verify the parameters you specified for the Neural Clusters mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this: 
Control name Value
Settings name Sample settings
Comment Data collected during 1st half of 1997
Mining function Neural Clusters
   
Input data Quality Food supermarket
Comment May transactions
Optimize mining run for Time
Filter records condition Conditions selected
Power options  
   
Use mode Clustering mode
Maximum passes 7
Maximum rows 5
Maximum columns 1
   
Active fields 6 selected
Supplementary fields 3 selected
Filter mask customer*
   
Outlier treatment Treat outliers as missing values
   
Run the parallel mode of the function Use this number of parallel processes: 4
   
Output fields 1 selected
Cluster ID field name Cluster ID
Record score field name  
Cluster ID field name choice 2  
Record score field name choice 2  
Confidence field name  
   
Output data Clusters Quality Food supermarket
   
Result name Result Quality food supermarket
Comment May transactions
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


TR> idmem3au.htm  
Summary: Sequential Patterns mining function  
   
Use this page to verify the parameters you specified for the Sequential Patterns mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings name Sample settings
Comment Data collected during 1st half of 1997
Mining function Sequential Patterns
   
Input data Sunset retail June transactions
Optimize the mining run for Time
Filter records condition Conditions selected
Power options  
   
Transaction group field Customer ID
Transaction field Date and purchase sequence number
Item field Item ID
Filter mask customer*
Within each value in the Transaction group field, sort the input data on the values in the Transaction field False
 
Minimum support 5
Maximum pattern length 3
Item constraints Selected
Run the parallel mode of this function Use this number of parallel processes: 4
   
Taxonomy name Sunset non-food
Comment Different taxonomies in New York City, Washington D.C., and Cleveland
   
Results Sunset June sequential patterns
Comment Branch locations New York City
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


k2!.2^5p5q5 l2./usr/lpp/IMiner/html/idmem4au.htm idmem4au  
Summary: Time Sequences mining function  
   
Use this page to verify the parameters you specified for the Time Sequences mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.
 
The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings name Sample settings
Comment Data collected during 1st half of 1997
Mining function Time Sequences
   
Input data Sunset retail store
Comment Branch location Chicago
Optimize mining run for Time
Filter records condition Conditions selected
Power options  
   
Sequence field Beach wear
Time field Month
Time sequence value Sales
Filter mask Sports
   
Epsilon 0,2
Gap 8
Window size 16
Matching length 0,05
   
Result name Sunset time sequences
Comment Branch location Chicago
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


-> idmem5bu.htm  
Summary: Neural Classification mining function  
   
Use this page to verify the parameters you specified for the Neural Classification mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings Sample settings
Comment Data collected during 1st half of 1997
Mining function Neural Classification
   
Input data Insurance data Security First
Comment Attributes: Age, salary, marital status
Optimize mining run for Time
Filter records condition "Risk class"=Good
Power options  
   
Use mode Training mode
In-sample size 4
Out-sample size 2
Maximum number of passes 500
Accuracy 80
Error rate 20
Regardless of the mode, normalize the input data True
   
Input fields 4 selected
Class label Risk class
Filter mask *class*
   
Architecture determination Manual
Hidden units 1 2
Hidden units 2 3
Hidden units 3 2
Parameter determination Manual
Learn rate 0.2
Momentum 0.9
   
Run the parallel mode of the function 4 processes
   
Output fields 3 selected
Class ID field name Risk class
Confidence field name  
   
Output data Output risk classes Security First
Comment Customers who allowed their insurance to lapse
   
Result name Results Security First risk classes
Comment Customers who allowed their insurance to lapse
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


 
kj0^5p5q5 p./usr/lpp/IMiner/html/idmem5du.htm idmem5du.htm  
Summary: Tree Classification mining function  
   
Use this page to verify the parameters you specified for the Tree Classification mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings Sample settings
Comment Data collected during 1st half of 1997
Mining function Tree Classification
   
Input data Insurance data Security First
Comment Attributes: Age, salary, marital status
Optimize mining run for Time
Filter records condition "Risk class"=Good
Power options None
   
Use mode Training mode
Maximum tree depth 5
Maximum purity per internal node 90
Minimum records per internal node 7
Classify result None selected
   
Input fields 4 selected
Class label Risk class
Filter mask *class*
   
Field weights 5 selected
   
Run the parallel mode of the function Use 4 parallel processes
   
Output fields 3 selected
Class ID field name Risk class
Confidence field name  
   
Output data Output Security First risk classes
   
Result Result Security First risk classes
Comment Customers who allowed their insurance to lapse
If a result with this name exists, overwrite it False
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


 
NT kf1^5p5q5 r./usr/lpp/IMiner/html/idmem6bu.htm idmem6bu.htm  
Summary: Neural Prediction mining function  
   
Use this page to verify the parameters you specified for the Neural Prediction mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings Sample settings
Comment Data collected during 1st half of 1997
Mining function Neural Prediction
   
Input data Insurance data Security First
Comment Branch office Cleveland
Optimize mining run for Time
Filter records condition "Revenue">50.000
Power options  
   
Use mode Training mode
In-sample size 7
Out-sample size 4
Maximum number of passes 500
Forecast horizon 0
Window size 1
Average error 0.1
Regardless of mode, normalize the input data True
   
Active fields 2 selected
Supplementary fields 1 selected
Prediction field Revenue
Filter mask None
   
List of values to predict Selected
   
Architecture determination Manual
Hidden units 1 2
Hidden units 2 3
Hidden units 3 2
Parameter determination Manual
Learn rate 0.2
Momentum 0.9
Generate quantiles True
List of quantile limits 2, 10, 25, 50, 75, 90, 98
Run the parallel mode of the function On 4 processor nodes 
   
Output fields 1 selected
Predicted value field name Predicted value
Lower quantile field name Lower quantile
Upper quantile field name Upper quantile
Output data Predicted sales revenue
   
Result name Results insurance data Security First
Comment Branch office Cleveland only
If a result with this name exists, overwrite it True
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


 
kM2^5p5q5 t./usr/lpp/IMiner/html/idmem6cp.htm idmem6cp.htm  
Output fields: RBF Prediction mining function
   
Use this page to specify whether to create output data.

Hints and tips are available for sorting the fields in the Available fields list.

You must specify a valid name for the following output fields:

If you choose to generate quantiles, the output data also includes the following output fields, which field names you have to specify:
  • Lower quantile
  • Upper quantile
From the Available fields list, you can select additional output fields to be included in the output data.

Hints and tips

 In the Available fields list, you can sort the fields by ascending or descending order. Click the right mouse button and move the cursor over the displayed selection field.

Filtering the available fields

You can filter particular data fields in the Available fields list by specifying a field name in the Filter mask entry field. You can also use wildcard characters.

Example:You might want to look for data fields related to customers. The filter condition looks like this: 
Filter mask
*customer*
To redisplay all data fields in the Available fields list, delete *customer* and press Enter.

Moving fields between lists

To move fields from the Available fields list to the Output fields list:
  • Click the >> push button to move all  fields.

  • Select the fields to be moved and click the > push button to move the selected  fields.

Example: You might want to specify the following field names for the output fields: 
Output fields Predicted value field name Region ID field name Lower quantile field name Upper quantile field name
Customer number Predicted revenue Region ID Lower quantile Upper quantile
 


 ko3&_5p5q5 u&./usr/lpp/IMiner/html/idmem6du.htm idmem6du.htm  
Summary: RBF Prediction mining function  
   
Use this page to verify the parameters you specified for the RBF Prediction mining function, to change one of the parameters, to save the settings, or to start the mining function immediately.

The summary of your specification for the advanced pages and controls might look like this:  
Control name Value
Settings Sample settings
Comment Data collected during 1st half of 1997
Mining function RBF Prediction
   
Input data Insurance data Security First
Comment Branch office Cleveland only
Optimize mining run for Time
Filter records condition "Revenue">50.000
Power options  
   
Use mode Training mode
In-sample size 12
Out-sample size 12
Maximum number of passes 25
Maximum centers 500
Minimum region size 20
Minimum passes 5
   
Active fields 2 selected
Supplementary fields 1 selected
Prediction field Revenue
Filter mask Customer
   
Generate quantiles True
List of quantile limits 2, 10, 25, 50, 75, 90, 98
   
Output fields 1 selected
Predicted value field name Predicted revenue
Region ID field name Region ID
Lower quantile field name Lower quantile
Upper quantile field name Upper quantile
Output data Output sales revenue
   
Result name Predicted sales revenue
Comment  
If a result with this name exists, overwrite it True
 
 
After you have saved the settings for later use, this TaskGuide is closed. Remember to save the mining base from the main window before closing it.

You can use the settings individually or as part of a sequence.


 
>< k~4B _5p5q5 wB ./usr/lpp/IMiner/html/idmep99o.htm idmep00o.htm  
Create settings from output data: Processing functions
   
 
This window allows you to create an Intelligent Miner data object from the database table or database view that a processing function uses as the output data. You can create a data object from the output data of all processing functions except for the Run SQL, Clean Up Data Sources, and Copy Records to File processing functions. 

When you specify output data for a processing function, you either select an existing Intelligent Miner data object or specify the schema name and table or view name. 

If you specified a schema name and table or view name for the output data, you can use this window to create an Intelligent Miner data object that refers to this database table or view. 
 
You might enter the following parameters to create a data object from the database table containing the output data for the current processsing function: 
 
Settings name
Comment
Use mode
Filtered Customers Customer data filtered for purchases over $100 Read and write
 
/Xk$ 09use this window to create an Intelligent Miner data object that refers to this database table or view.    You might enter the following parameters to create a data object from the database table containing the output data for the current processsing function: