# MiningNodeType Enumeration

Represents the type of the MiningContentNode.

**Namespace:**Microsoft.AnalysisServices.AdomdServer

**Assembly:**msmgdsrv (in msmgdsrv.dll)

Member name | Description | |
---|---|---|

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. This type is used by plug-in algorithms. (1000) | |

Distribution | The node represents the terminal node, or leaf node, 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) |

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