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Purpose
Context Administration Classification Training Service
AUTHID
CURRENT_USER
Constants
Name
Data Type
Value
General
SENTIMENT_NUL
NUMBER
0
SENTIMENT_POS
NUMBER
1
SENTIMENT_NEG
NUMBER
2
Data Types
TYPE doc_rec IS RECORD (
docid NUMBER, -- document ID to identify the document
clusterid NUMBER, -- the ID of the cluster the document is assigned to
score NUMBER); -- the similarity score between document and cluster
TYPE doc_tab IS TABLE OF doc_rec INDEX BY BINARY_INTEGER;
/* in-memory table for cluster information */
TYPE cluster_rec IS RECORD (
clusterid NUMBER, -- cluster ID to identify a cluster
descript drvutl.dr_extrabuf, --string to describe the cluster
label drvutl.dr_longbuf, -- a suggested label for the cluster
sze NUMBER, -- number of documents assigned to the cluster
quality_score NUMBER, -- the quality score of the cluster
parent NUMBER); -- parent cluster id. negative means no parent
TYPE cluster_tab IS TABLE OF cluster_rec INDEX BY BINARY_INTEGER;
TYPE docid_tab IS TABLE OF NUMBER INDEX BY BINARY_INTEGER;
Dependencies
DBMS_ASSERT
DRIPREF
DRVODM
DBMS_STANDARD
DRIUTL
DRVTMT
DRIACC
DRIVAL
DRVUTL
DRICON
DRIXMD
DRVXMD
DRIG
DRUE
DR_DEF
DRILIST
DRVDISP
PLITBLM
DRIOBJ
DRVIMR
Documented
Yes
First Available
Not known
Security Model
Owned by CTXSYS with EXECUTE granted to MDSYS and PUBLIC
Direct access to this package is prevented by means of an Accessible By clause. CTX_CLS can be accessed through the SVM_TRAIN procedure.
Generates a set of sub-group (clusters) from a provided collection of documents
Overload 1
ctx_cls.clustering(
index_name IN VARCHAR2,
docid IN VARCHAR2,
doctab_name IN VARCHAR2,
clstab_name IN VARCHAR2,
pref_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(clustering, AUTO);
TBD
Generates a set of sub-group (clusters) from a provided collection of documents
Overload 2
ctx_cls.clustering(
index_name IN VARCHAR2,
docid IN VARCHAR2,
dids IN docid_tab,
doctab_name IN OUT NOCOPY doc_tab,
clstab_name IN OUT NOCOPY cluster_tab,
pref_name IN VARCHAR2 DEFAULT NULL);
Generates sentiment model based on provided training set data
ctx_cls.sa_train_model(
clsfier_name IN VARCHAR2,
index_name IN VARCHAR2,
docid IN VARCHAR2,
cattab IN VARCHAR2,
catdocid IN VARCHAR2,
catid IN VARCHAR2,
pref_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(sa_train_model, AUTO);
Automatically generates ctx-rules from training examples
Overload 1
ctx_cls.train(
index_name IN VARCHAR2,
docid IN VARCHAR2,
cattab IN VARCHAR2,
catdocid IN VARCHAR2,
catid IN VARCHAR2,
restab IN VARCHAR2,
rescatid IN VARCHAR2,
resquery IN VARCHAR2,
resconfid IN VARCHAR2,
pref_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(train, AUTO);
TBD
Automatically generates predicative model from examples for classification
Overload 2
ctx_cls.train(
index_name IN VARCHAR2,
docid IN VARCHAR2,
cattab IN VARCHAR2,
catdocid IN VARCHAR2,
catid IN VARCHAR2,
restab IN VARCHAR2,
pref_name IN VARCHAR2);
PRAGMA SUPPLEMENTAL_LOG_DATA(train, AUTO);
