Accord.NET Framework

## KModes Class |

k-Modes algorithm.

Inheritance Hierarchy

SystemObject

Accord.MachineLearningParallelLearningBase

Accord.MachineLearningKModesInt32

Accord.MachineLearningKModes

Accord.MachineLearningParallelLearningBase

Accord.MachineLearningKModesInt32

Accord.MachineLearningKModes

Syntax

The KModes type exposes the following members.

Constructors

Properties

Name | Description | |
---|---|---|

Clusters |
Gets the clusters found by K-modes.
(Inherited from KModesT.) | |

ComputeError |
Gets or sets whether the clustering distortion error (the
average distance between all data points and the cluster
centroids) should be computed at the end of the algorithm.
The result will be stored in Error. Default is true.
(Inherited from KModesT.) | |

Dimension |
Gets the dimensionality of the data space.
(Inherited from KModesT.) | |

Distance |
Gets or sets the distance function used
as a distance metric between data points.
(Inherited from KModesT.) | |

Error |
Gets the cluster distortion error (the average distance
between data points and the cluster centroids) after the
last call to this class' Compute methods.
(Inherited from KModesT.) | |

Initialization |
Gets or sets the strategy used to initialize the
centroids of the clustering algorithm. Default is
KMeansPlusPlus.
(Inherited from KModesT.) | |

Iterations |
Gets the number of iterations performed in the
last call to this class' Compute methods.
(Inherited from KModesT.) | |

K |
Gets the number of clusters.
(Inherited from KModesT.) | |

MaxIterations |
Gets or sets the maximum number of iterations to
be performed by the method. If set to zero, no
iteration limit will be imposed. Default is 0.
(Inherited from KModesT.) | |

ParallelOptions |
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.) | |

Token |
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.) | |

Tolerance |
Gets or sets the relative convergence threshold
for stopping the algorithm. Default is 1e-5.
(Inherited from KModesT.) |

Methods

Name | Description | |
---|---|---|

Compute | Obsolete.
Divides the input data into K clusters.
(Inherited from KModesT.) | |

Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |

Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |

GetHashCode | Serves as the default hash function. (Inherited from Object.) | |

GetType | Gets the Type of the current instance. (Inherited from Object.) | |

Learn |
Learns a model that can map the given inputs to the desired outputs.
(Inherited from KModesT.) | |

MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |

ToString | Returns a string that represents the current object. (Inherited from Object.) |

Extension Methods

Name | Description | |
---|---|---|

HasMethod |
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) | |

IsEqual | Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.) | |

ToT |
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |

Remarks

The k-Modes algorithm is a variant of the k-Means which instead of locating means attempts to locate the modes of a set of points. As the algorithm does not require explicit numeric manipulation of the input points (such as addition and division to compute the means), the algorithm can be used with arbitrary (generic) data structures.

This is the specialized, non-generic version of the K-Modes algorithm that is set to work on Int32 arrays.

See Also