RODM_create_kmeans_model: Create a Hierarchical k-means model

In RODM: R interface to Oracle Data Mining

Description

This function creates a Hierarchical k-means model.

Usage

RODM_create_kmeans_model(database, 
                         data_table_name, 
                         case_id_column_name = NULL, 
                         model_name = "KM_MODEL",
                         auto_data_prep = TRUE,
                         num_clusters = NULL, 
                         block_growth = NULL, 
                         conv_tolerance = NULL,
                         euclidean_distance = TRUE,
                         iterations = NULL, 
                         min_pct_attr_support = NULL,
                         num_bins = NULL, 
                         variance_split = TRUE,
                         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.
auto_data_prep	Whether or not ODM should invoke automatic data preparation for the build.
num_clusters	Setting that specifies the number of clusters for a clustering model.
block_growth	Setting that specifies the growth factor for memory to hold cluster data for k-Means.
conv_tolerance	Setting that specifies the convergence tolerance for k-Means.
euclidean_distance	Distance function (cosine, euclidean or fast_cosine).
iterations	Setting that specifies the number of iterations for k-Means.
min_pct_attr_support	Setting that specifies the minimum percent required for attributes in rules.
num_bins	Setting that specifies the number of histogram bins k-Means.
variance_split	Setting that specifies the split criterion for k-Means.
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

The algorithm k-means (kmeans) uses a distance-based similarity measure and tessellates the data space creating hierarchies. It handles large data volumes via summarization and supports sparse data. It is especially useful when the dataset has a moderate number of numerical attributes and one has a predetermined number of clusters. The main parameters settings correspond to the choice of distance function (e.g., Euclidean or cosine), number of iterations, convergence tolerance and split criterion.

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 and 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.
km.clusters	General per-cluster information.
km.taxonomy	Parent-child cluster relationship.
km.centroid	Per cluster-attribute centroid information.
km.histogram	Per cluster-attribute hitogram information.
km.rule	Cluster rules.
km.leaf_cluster_count	Leaf clusters with support.
km.assignment	Assignment of training data to clusters (with probability).

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

Oracle Database SQL Language Reference (Data Mining functions) 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/server.111/b28286/functions001.htm#SQLRF20030

See Also

RODM_apply_model, RODM_drop_model

Examples

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

### Clustering a 2D multi-Gaussian distribution of points into clusters

set.seed(seed=6218945)
X1 <- c(rnorm(100, mean = 2, sd = 1), rnorm(100, mean = 8, sd = 2), rnorm(100, mean = 5, sd = 0.6),
        rnorm(100, mean = 4, sd = 1), rnorm(100, mean = 10, sd = 1)) # Create and merge 5 Gaussian distributions
Y1 <- c(rnorm(100, mean = 1, sd = 2), rnorm(100, mean = 4, sd = 1.5), rnorm(100, mean = 6, sd = 0.5),
        rnorm(100, mean = 3, sd = 0.2), rnorm(100, mean = 2, sd = 1))
ds <- data.frame(cbind(X1, Y1)) 
n.rows <- length(ds[,1])                                                    # Number of rows
row.id <- matrix(seq(1, n.rows), nrow=n.rows, ncol=1, dimnames= list(NULL, c("ROW_ID"))) # Row id
ds <- cbind(row.id, ds)                                                     # Add row id to dataset 
RODM_create_dbms_table(DB, "ds")   

km <- RODM_create_kmeans_model(
   database = DB,                  # database ODBC channel identifier
   data_table_name = "ds",         # data frame containing the input dataset
   case_id_column_name = "ROW_ID", # case id to enable assignments during build
   num_clusters = 5)

km2 <- RODM_apply_model(
   database = DB,                  # database ODBC channel identifier
   data_table_name = "ds",         # data frame containing the input dataset
   model_name = "KM_MODEL",
   supplemental_cols = c("X1","Y1"))

x1a <- km2$model.apply.results[, "X1"]
y1a <- km2$model.apply.results[, "Y1"]
clu <- km2$model.apply.results[, "CLUSTER_ID"]
c.numbers <- unique(as.numeric(clu))
c.assign <- match(clu, c.numbers)
color.map <- c("blue", "green", "red", "orange", "purple")
color <- color.map[c.assign]
nf <- layout(matrix(c(1, 2), 1, 2, byrow=T), widths = c(1, 1), heights = 1, respect = FALSE)
plot(x1a, y1a, pch=20, col=1, xlab="X1", ylab="Y1", main="Original Data Points")
plot(x1a, y1a, pch=20, type = "n", xlab="X1", ylab="Y1", main="After kmeans clustering")
for (i in 1:n.rows) {
   points(x1a[i], y1a[i], col= color[i], pch=20)
}   
legend(5, -0.5, legend=c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5"), pch = rep(20, 5), 
       col = color.map, pt.bg = color.map, cex = 0.8, pt.cex=1, bty="n")

km        # look at the model details and cluster assignments

RODM_drop_model(DB, "KM_MODEL")   # Drop the model
RODM_drop_dbms_table(DB, "ds")    # Drop the database table

RODM_close_dbms_connection(DB)

## End(Not run)

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