MiningNodeType Enumeration
Represents the type of the MiningContentNode.
Namespace: Microsoft.AnalysisServices.AdomdClient
Assembly: Microsoft.AnalysisServices.AdomdClient (in microsoft.analysisservices.adomdclient.dll)
Syntax
'Declaration
Public Enumeration MiningNodeType
public enum MiningNodeType
public enum class MiningNodeType
public enum MiningNodeType
public enum MiningNodeType
Members
| Member name | Description |
|---|---|
| AssociationRule | The node represents an association rule detected by the algorithm. |
| Cluster | The node represents a cluster detected by the algorithm. |
| 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. |
| Distribution | The node represents a leaf of a classification tree. |
| InputAttribute | The node corresponds to a predictable attribute. |
| InputAttributeState | The node contains statistics about the states of an input attribute. |
| Interior | The node represents an interior split node in a classification tree. |
| ItemSet | The node represents an itemset detected by the algorithm |
| Model | The root content node. This node applies to all algorithms. |
| NaiveBayesMarginalStatNode | The node containing marginal statistics about the training set, stored in a format used by the Naïve Bayes algorithm. |
| NNetHiddenLayer | The node which groups together the nodes that describe the hidden layer. This type is used with neural network algorithms. |
| NNetHiddenNode | The node is a node of the hidden layer. This type is used with neural network algorithms. |
| NNetInputLayer | The node which groups together the nodes of the input layer. This type is used with neural network algorithms. |
| 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. |
| 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. |
| NNetOutputLayer | The node which groups together the nodes of the output layer. This type is used with neural network algorithms. |
| 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. |
| NNetSubnetwork | The node contains one sub-network. This type is used with neural network algorithms. |
| PredictableAttribute | The node corresponds to a predictable attribute. |
| RegressionTreeRoot | The node is the root of a regression tree. |
| 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. |
| TimeSeries | The non-root node of a time series tree. |
| 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. |
| Tree | The node is the root node of a classification tree. |
| TsTree | The root node of a time series tree that corresponds to a predictable time series. |
| Unknown | An unknown node type. |
Platforms
Development Platforms
For a list of the supported platforms, see Hardware and Software Requirements for Installing SQL Server 2005.
Target Platforms
For a list of the supported platforms, see Hardware and Software Requirements for Installing SQL Server 2005.