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Énumération MiningNodeType

Represents the type of the MiningContentNode.

Espace de noms :  Microsoft.AnalysisServices.AdomdClient
Assembly :  Microsoft.AnalysisServices.AdomdClient (en Microsoft.AnalysisServices.AdomdClient.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
Model The root content node. This node applies to all algorithms. (1)
Tree The node is the root node of a classification tree. (2)
Interior The node represents an interior split node in a classification tree. (3)
Distribution The node represents a leaf of a classification tree. (4)
Cluster The node represents a cluster detected by the algorithm. (5)
Unknown An unknown node type. (6)
ItemSet The node represents an itemset detected by the algorithm. (7)
AssociationRule The node represents an association rule detected by the algorithm. (8)
PredictableAttribute The node corresponds to a predictable attribute. (9)
InputAttribute The node corresponds to a predictable attribute. (10)
InputAttributeState The node contains statistics about the states of an input attribute. (11)
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)
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)
TimeSeries The non-root node of a time series tree. (15)
TsTree The root node of a time series tree that corresponds to a predictable time series. (16)
NNetSubnetwork The node contains one sub-network. This type is used with neural network algorithms. (17)
NNetInputLayer The node that groups together the nodes of the input layer. This type is used with neural network algorithms. (18)
NNetHiddenLayer The node that groups together the nodes that describe the hidden layer. This type is used with neural network algorithms. (19)
NNetOutputLayer The node that groups together the nodes of the output layer. This type is used with neural network algorithms. (21)
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)
NNetHiddenNode The node is a node of the hidden layer. This type is used with neural network algorithms. (22)
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)
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)
RegressionTreeRoot The node is the root of a regression tree. (25)
NaiveBayesMarginalStatNode The node containing marginal statistics about the training set, stored in a format used by the Naïve Bayes algorithm. (26)
ArimaRoot The root node of an ARIMA model. (27)
ArimaPeriodicStructure The node that represents a periodic structure in an ARIMA model. (28)
ArimaAutoRegressive The node that contains the autoregressive coefficient for a single term in an ARIMA model. (29)
ArimaMovingAverage The node that contains the moving average coefficient for a single term in an ARIMA model. (30)
CustomBase Represents the starting point for custom node types. Custom node types must be integers greater in value than this constant. (1000) This type is used by plug-in algorithms.

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.