Enumeração MiningNodeType
Represents the type of the MiningContentNode.
Namespace: Microsoft.AnalysisServices.AdomdClient
Assembly: Microsoft.AnalysisServices.AdomdClient (em Microsoft.AnalysisServices.AdomdClient.dll)
Sintaxe
'Declaração
Public Enumeration MiningNodeType
'Uso
Dim instance As MiningNodeType
public enum MiningNodeType
public enum class MiningNodeType
type MiningNodeType
public enum MiningNodeType
Membros
| Nome do membro | Descrição | |
|---|---|---|
| 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. |
Comentários
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.