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Énumération MiningNodeType

Represents the type of the MiningContentNode.

Espace de noms :  Microsoft.AnalysisServices.AdomdServer
Assembly :  msmgdsrv (dans msmgdsrv.dll)

Syntaxe

'Déclaration
Public Enumeration MiningNodeType
'Utilisation
Dim instance As MiningNodeType
public enum MiningNodeType
public enum class MiningNodeType
type MiningNodeType
public enum MiningNodeType

Membres

Nom de membre Description
CustomBase Represents the starting point for custom node types. Custom node types must be integers greater in value than this constant. This type is used by plug-in algorithms.

(1000)

ArimaMovingAverage The node that contains the moving average coefficient for a single term in an ARIMA model. (30)
ArimaAutoRegressive The node that contains the autoregressive coefficient for a single term in an ARIMA model. (29)
ArimaPeriodicStructure The node that represents a periodic structure in an ARIMA model. (28)
ArimaRoot The root node of an ARIMA model. (27)
NaiveBayesMarginalStatNode The node containing marginal statistics about the training set, stored in a format used by the Naïve Bayes algorithm. (26)
RegressionTreeRoot The node is the root of a regression tree. (25)
NNetMarginalNode The node containing marginal statistics about the training set, stored in a format used by the algorithm. This type is used with neural network algorithms. (24)
NNetOutputNode The node is a node of the output layer. This node will usually match an output attribute and the corresponding states. This type is used with neural network algorithms. (23)
NNetHiddenNode The node is a node of the hidden layer. This type is used with neural network algorithms. (22)
NNetInputNode The node is a node of the input layer. This node will usually match an input attribute and the corresponding states. This type is used with neural network algorithms. (21)
NNetOutputLayer The node that groups together the nodes of the output layer. This type is used with neural network algorithms. (21)
NNetHiddenLayer The node that groups together the nodes that describe the hidden layer. This type is used with neural network algorithms. (19)
NNetInputLayer The node that groups together the nodes of the input layer. This type is used with neural network algorithms. (18)
NNetSubnetwork The node contains one sub-network. This type is used with neural network algorithms. (17)
TsTree The root node of a time series tree that corresponds to a predictable time series. (16)
TimeSeries The non-root node of a time series tree. (15)
Transition The node representing a row of a Markov transition matrix. This node will have a node of type Sequence as a parent, and no children. (14)
InputAttributeState The node contains statistics about the states of an input attribute. (11)
InputAttribute The node corresponds to a predictable attribute. (10)
PredictableAttribute The node corresponds to a predictable attribute. (9)
AssociationRule The node represents an association rule detected by the algorithm. (8)
ItemSet The node represents an itemset detected by the algorithm. (7)
Cluster The node represents a cluster detected by the algorithm. (5)
Interior The node represents an interior split node in a classification tree. (3)
Tree The node is the root node of a classification tree. (2)
Model The root content node. This node applies to all algorithms. (1)
Distribution The node represents the terminal node, or leaf node, of a classification tree. (4)
Sequence The top node for a Markov model component of a sequence cluster. This node will have a node of type Cluster as a parent, and children of type Transition. (13)
Unknown An unknown node type. (6)

Notes

When you retrieve nodes from mining model content, the node type may be returned as an integer value that represents the enumeration. These integer values are provided in parentheses.