RODM_create_oc_model: Create an O-cluster model

In RODM: R interface to Oracle Data Mining

Description

This function creates a O-cluster model.

Usage

RODM_create_oc_model(database, 
                         data_table_name, 
                         case_id_column_name,
                         model_name = "OC_MODEL",
                         auto_data_prep = TRUE,
                         num_clusters = NULL, 
                         max_buffer = NULL,
                         sensitivity = 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.
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 the clustering model.
max_buffer	Buffer size for O-Cluster. Default is 50,000.
sensitivity	A fraction that specifies the peak density required for separating a new cluster. The fraction is related to the global uniform density. Default is 0.5.
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 O-Cluster algorithm creates a hierarchical grid-based clustering model, that is, it creates axis-parallel (orthogonal) partitions in the input attribute space. The algorithm operates recursively. The resulting hierarchical structure represents an irregular grid that tessellates the attribute space into clusters. The resulting clusters define dense areas in the attribute space.

The clusters are described by intervals along the attribute axes and the corresponding centroids and histograms. A parameter called sensitivity defines a baseline density level. Only areas with peak density above this baseline level can be identified as clusters.

The k-means algorithm tessellates the space even when natural clusters may not exist. For example, if there is a region of uniform density, k-Means tessellates it into n clusters (where n is specified by the user). O-Cluster separates areas of high density by placing cutting planes through areas of low density. O-Cluster needs multi-modal histograms (peaks and valleys). If an area has projections with uniform or monotonically changing density, O-Cluster does not partition it.

The clusters discovered by O-Cluster are used to generate a Bayesian probability model that is then used during scoring (model apply) for assigning data points to clusters. The generated probability model is a mixture model where the mixture components are represented by a product of independent normal distributions for numerical attributes and multinomial distributions for categorical attributes.

Keep the following in mind if you choose to prepare the data for O-Cluster: 1. O-Cluster does not necessarily use all the input data when it builds a model. It reads the data in batches (the default batch size is 50000). It will only read another batch if it believes, based on statistical tests, that there may still exist clusters that it has not yet uncovered. 2. Because O-Cluster may stop the model build before it reads all of the data, it is highly recommended that the data be randomized. 3. Binary attributes should be declared as categorical. O-Cluster maps categorical data to numerical values. 4. The use of Oracle Data Mining's equi-width binning transformation with automated estimation of the required number of bins is highly recommended. 5. The presence of outliers can significantly impact clustering algorithms. Use a clipping transformation before binning or normalizing. Outliers with equi-width binning can prevent O-Cluster from detecting clusters. As a result, the whole population appears to falls within a single cluster.

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

Author(s)

Pablo Tamayo [e-mail]

Ari Mozes [e-mail]

References

B.L. Milenova and M.M. Campos, Clustering Large Databases with Numeric and Nominal Values Using Orthogonal Projection, Proceeding of the 29th VLDB Conference, Berlin, Germany (2003).

Oracle9i O-Cluster: Scalable Clustering of Large High Dimensional Data Sets http://www.oracle.com/technology/products/bi/odm/pdf/o_cluster_algorithm.pdf

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")   

oc <- RODM_create_oc_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)

oc2 <- RODM_apply_model(
   database = DB,                  # database ODBC channel identifier
   data_table_name = "ds",         # data frame containing the input dataset
   model_name = "OC_MODEL",
   supplemental_cols = c("X1","Y1"))

x1a <- oc2$model.apply.results[, "X1"]
y1a <- oc2$model.apply.results[, "Y1"]
clu <- oc2$model.apply.results[, "CLUSTER_ID"]
c.numbers <- unique(as.numeric(clu))
c.assign <- match(clu, c.numbers)
color.map <- c("blue", "green", "red")
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 OC 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"), pch = rep(20, 3), 
       col = color.map, pt.bg = color.map, cex = 0.8, pt.cex=1, bty="n")

oc        # look at the model details and cluster assignments

RODM_drop_model(DB, "OC_MODEL")   # Drop the database table
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.