RODM_create_assoc_model(RODM)
RODM_create_assoc_model()所属R语言包：RODM
Create an Association Rules model
创建一个关联规则模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function creates an Association Rules model.
这个函数创建一个关联规则模型。
用法----------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)
参数----------Arguments----------
参数：database
Database ODBC channel identifier returned from a call to RODM_open_dbms_connection
数据库的ODBC通道标识符返回调用RODM_open_dbms_connection
参数：data_table_name
Database table/view containing the training dataset.
数据库表/视图包含训练数据集。
参数：case_id_column_name
Row unique case identifier in data_table_name.
行独特的标识符的data_table_name。
参数：model_name
ODM Model name.
ODM产品型号名称。
参数：min_support
Setting that specifies the minimum support for assoc.
设置指定的最小支持度为副教授。
参数：min_confidence
Setting that specifies the minimum confidence for assoc.
设置指定的最小置信度ASSOC。
参数：max_rule_length
Setting that specifies the maximum rule length for assoc.
设置指定的最大规则长度为副教授。
参数：retrieve_outputs_to_R
Flag controlling if the output results are moved to the R environment.
船籍控制，如果输出的结果被移动到R环境。
参数：leave_model_in_dbms
Flag controlling if the model is deleted or left in RDBMS.
如果模型被删除或留在RDBMS标志控制。
参数：sql.log.file
File where to append the log of all the SQL calls made by this function.
文件中追加的log所有的SQL调用此功能。
Details
详细信息----------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.
此功能实现的Apriori算法（阿格拉瓦尔和Srikant，1994）发现频繁项集产生关联模型（AM）。发现发生的“交易”数据，如市场购物篮分析的情况下，大量的项目合作。该规则的含义是，在交易记录的一组项目的外观意味着另一组的项目。根据他们经常发生的（支持），以及如何往往随之而来的遵循先行（置信度），形成规则的项目组必须通过的最低阈值。关联模型产生所有规则有支持和信心大于用户指定的阈值。 AM算法是有效的，规模以及有关的交易数量，件数，数集和规则产生。
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.
有关algotithm的实现的详细信息，参数设置和的ODM函数本身的特性请咨询以下Oracle文档：ODM的概念，ODM开发的指南，Oracle的SQL套件：数据挖掘，和甲骨文数据库SQL语言参考（数据挖掘功能），上市在下面的参考资料。
值----------Value----------
If retrieve_outputs_to_R is TRUE, returns a list with the following elements: <table summary="R valueblock"> <tr valign="top"><td>model.model_settings</td> <td> Table of settings used to build the model.</td></tr> <tr valign="top"><td>model.model_attributes</td> <td> Table of attributes used to build the model.</td></tr> <tr valign="top"><td>ar.rules</td> <td> List of the association rules.</td></tr> <tr valign="top"><td>ar.itemsets</td> <td> List of the frequent itemsets.</td></tr> </table>
如果retrieve_outputs_to_R是TRUE，返回一个列表，包含下列元素：<table summary="R valueblock"> <tr valign="top"> <TD> model.model_settings</ TD> <TD>表，用来设置建立模型。</ TD> </ TR> <tr valign="top"> <TD> model.model_attributes</ TD> <TD>表用于建立模型的属性。</ TD> </ TR> <tr valign="top"> <TD>ar.rules </ TD>的关联规则<TD>列表。</ TD> </ TR> <tr valign="top"> <TD> ar.itemsets</ TD>频繁项目集<TD>列表。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Pablo Tamayo <a href="mailto:pablo.tamayo@oracle.com">pablo.tamayo@oracle.com</a>
Ari Mozes <a href="mailto:ari.mozes@oracle.com">ari.mozes@oracle.com</a>
参考文献----------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
参见----------See Also----------
实例----------Examples----------
## Not run: [＃不运行：]
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[选择的属性子集]
ards[,] <- ifelse(ards[,] == 0, NA, "YES") # make it sparse, as required by ODM[它稀疏的要求，ODM]
n.rows <- length(ards[,1]) # Number of rows[行数]
row.id <- matrix(seq(1, n.rows), nrow=n.rows, ncol=1, dimnames= list(NULL, c("ROW_ID"))) # Row id[行ID]
ards <- cbind(row.id, ards) # Add row id to dataset [添加行号数据集]
RODM_create_dbms_table(DB, "ards") # Push the training table to the database[推训练表到数据库]
# Build the association rules model[建立关联规则模型]
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[检查的内容的ar找到的规则和项集]
RODM_drop_model(DB, "AR_MODEL")
RODM_drop_dbms_table(DB, "ards")
RODM_close_dbms_connection(DB)
## End(Not run)[＃（不执行）]
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-27 22:50:50
