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).