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MSDN Library

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
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)

ArimaPeriodicStructure

The node that represents a periodic structure in an ARIMA model. (28)

ArimaRoot

The root node of an ARIMA model. (27)

AssociationRule

The node represents an association rule detected by the algorithm. (8)

Cluster

The node represents a cluster detected by the algorithm. (5)

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.

Distribution

The node represents a leaf of a classification tree. (4)

InputAttribute

The node corresponds to a predictable attribute. (10)

InputAttributeState

The node contains statistics about the states of an input attribute. (11)

Interior

The node represents an interior split node in a classification tree. (3)

ItemSet

The node represents an itemset detected by the algorithm. (7)

Model

The root content node. This node applies to all algorithms. (1)

NaiveBayesMarginalStatNode

The node containing marginal statistics about the training set, stored in a format used by the Naïve Bayes algorithm. (26)

NNetHiddenLayer

The node that groups together the nodes that describe the hidden layer. This type is used with neural network algorithms. (19)

NNetHiddenNode

The node is a node of the hidden layer. This type is used with neural network algorithms. (22)

NNetInputLayer

The node that groups together the nodes of the input layer. This type is used with neural network algorithms. (18)

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)

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)

NNetOutputLayer

The node that groups together the nodes of the output layer. This type is used with neural network algorithms. (21)

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)

NNetSubnetwork

The node contains one sub-network. This type is used with neural network algorithms. (17)

PredictableAttribute

The node corresponds to a predictable attribute. (9)

RegressionTreeRoot

The node is the root of a regression tree. (25)

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)

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)

Tree

The node is the root node of a classification tree. (2)

TsTree

The root node of a time series tree that corresponds to a predictable time series. (16)

Unknown

An unknown node type. (6)

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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