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MiningNodeType Enumeration

Represents the type of the MiningContentNode.

Namespace:  Microsoft.AnalysisServices.AdomdClient
Assembly:  Microsoft.AnalysisServices.AdomdClient (in Microsoft.AnalysisServices.AdomdClient.dll)

public enum MiningNodeType

Member nameDescription
ModelThe root content node. This node applies to all algorithms. (1)
TreeThe node is the root node of a classification tree. (2)
InteriorThe node represents an interior split node in a classification tree. (3)
DistributionThe node represents a leaf of a classification tree. (4)
ClusterThe node represents a cluster detected by the algorithm. (5)
UnknownAn unknown node type. (6)
ItemSetThe node represents an itemset detected by the algorithm. (7)
AssociationRuleThe node represents an association rule detected by the algorithm. (8)
PredictableAttributeThe node corresponds to a predictable attribute. (9)
InputAttributeThe node corresponds to a predictable attribute. (10)
InputAttributeStateThe node contains statistics about the states of an input attribute. (11)
SequenceThe 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)
TransitionThe 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)
TimeSeriesThe non-root node of a time series tree. (15)
TsTreeThe root node of a time series tree that corresponds to a predictable time series. (16)
NNetSubnetworkThe node contains one sub-network. This type is used with neural network algorithms. (17)
NNetInputLayerThe node that groups together the nodes of the input layer. This type is used with neural network algorithms. (18)
NNetHiddenLayerThe node that groups together the nodes that describe the hidden layer. This type is used with neural network algorithms. (19)
NNetOutputLayerThe node that groups together the nodes of the output layer. This type is used with neural network algorithms. (21)
NNetInputNodeThe 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)
NNetHiddenNodeThe node is a node of the hidden layer. This type is used with neural network algorithms. (22)
NNetOutputNodeThe 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)
NNetMarginalNodeThe 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)
RegressionTreeRootThe node is the root of a regression tree. (25)
NaiveBayesMarginalStatNodeThe node containing marginal statistics about the training set, stored in a format used by the Naïve Bayes algorithm. (26)
ArimaRootThe root node of an ARIMA model. (27)
ArimaPeriodicStructureThe node that represents a periodic structure in an ARIMA model. (28)
ArimaAutoRegressiveThe node that contains the autoregressive coefficient for a single term in an ARIMA model. (29)
ArimaMovingAverageThe node that contains the moving average coefficient for a single term in an ARIMA model. (30)
CustomBaseRepresents 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.

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.

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