RODM_create_assoc_model: Create an Association Rules model

In RODM: R interface to Oracle Data Mining

Description

This function creates an Association Rules model.

Usage

RODM_create_assoc_model(database, 
                        data_table_name, 
                        case_id_column_name,
                        model_name = "AR_MODEL",
                        min_support = NULL,
                        min_confidence = NULL,
                        max_rule_length = NULL,
                        retrieve_outputs_to_R = TRUE,
                        leave_model_in_dbms = TRUE, 
                        sql.log.file = NULL)

Arguments

database	Database ODBC channel identifier returned from a call to RODM_open_dbms_connection
data_table_name	Database table/view containing the training dataset.
case_id_column_name	Row unique case identifier in data_table_name.
model_name	ODM Model name.
min_support	Setting that specifies the minimum support for assoc.
min_confidence	Setting that specifies the minimum confidence for assoc.
max_rule_length	Setting that specifies the maximum rule length for assoc.
retrieve_outputs_to_R	Flag controlling if the output results are moved to the R environment.
leave_model_in_dbms	Flag controlling if the model is deleted or left in RDBMS.
sql.log.file	File where to append the log of all the SQL calls made by this function.

Details

This function implements the apriori algorithm (Agrawal and Srikant 1994) to find frequent itemsets and generate Association Models (AM). It finds the co-occurrence of items in large volumes of "transactional" data such as in the case of market basket analysis. The rule is an implication where the appearance of a set of items in a transactional record implies another set of items. The groups of items used to form rules must pass a minimum threshold according to how frequently they occur (support) and how often the consequent follows the antecedent (confidence). Association models generate all rules that have support and confidence greater than user-specified thresholds. The AM algorithm is efficient, and scales well with respect to the number of transactions, number of items, and number of itemsets and rules produced.

For more details on the algotithm implementation, parameters settings and characteristics of the ODM function itself consult the following Oracle documents: ODM Concepts, ODM Developer's Guide, Oracle SQL Packages: Data Mining, and Oracle Database SQL Language Reference (Data Mining functions), listed in the references below.

Value

If retrieve_outputs_to_R is TRUE, returns a list with the following elements:

model.model_settings	Table of settings used to build the model.
model.model_attributes	Table of attributes used to build the model.
ar.rules	List of the association rules.
ar.itemsets	List of the frequent itemsets.

Author(s)

Pablo Tamayo [e-mail]

Ari Mozes [e-mail]

References

Oracle Data Mining Concepts 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/toc.htm

Oracle Data Mining Application Developer's Guide 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28131/toc.htm

Oracle Data Mining Administrator's Guide 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28130/toc.htm

Oracle Database PL/SQL Packages and Types Reference 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/appdev.111/b28419/d_datmin.htm#ARPLS192

See Also

RODM_drop_model

Examples

## Not run: 
DB <- RODM_open_dbms_connection(dsn="orcl11g", uid= "rodm", pwd = "rodm")

data(satfruit, package="PASWR")
ards <- satfruit[,c("WH", "BA", "NAR", "COR", "SF", "VI", "PS", "ES", "AF", "CO", "AR", "AL", "OL")]  # Select subset of attributes
ards[,] <- ifelse(ards[,] == 0, NA, "YES")   # make it sparse, as required by ODM
n.rows <- length(ards[,1])           # Number of rows
row.id <- matrix(seq(1, n.rows), nrow=n.rows, ncol=1, dimnames= list(NULL, c("ROW_ID"))) # Row id
ards <- cbind(row.id, ards)          # Add row id to dataset 
RODM_create_dbms_table(DB, "ards")   # Push the training table to the database

# Build the association rules model
ar <- RODM_create_assoc_model(
 database = DB, 
 data_table_name = "ards", 
 case_id_column_name = "ROW_ID")

# Inspect the contents of ar to find the rules and itemsets

RODM_drop_model(DB, "AR_MODEL") 
RODM_drop_dbms_table(DB, "ards") 

RODM_close_dbms_connection(DB)

## End(Not run)

RODM documentation built on May 2, 2019, 7:03 a.m.