This class provides a base class for implementation for different Cluster Analysis algorithms. More...

Inheritance diagram for AbstractModel:

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Collaboration diagram for AbstractModel:

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Public Member Functions
_.Library.Integer	ById (_.Library.RawString id)
	Returns the ordinal number of the point with the given ID id. More...

_.Library.Double	Distance (_.Library.Integer i, _.Library.Integer j, _.Library.Double p, _.Library.Boolean normalize)
	Returns the dissimilarity measure between two data points of the model. More...

_.Library.Double	Distance1 (_.Library.Integer i, z, _.Library.Double p, _.Library.Boolean normalize)
	Returns the dissimilarity measure between a data points of the model and a point with given coordinates. More...

_.Library.Double	Distance12 (z1, z2, _.Library.Double p, _.Library.Boolean normalize)
	Returns the dissimilarity measure between two points with given coordinates. More...

_.DeepSee.extensions.clusters.ASW	GetASWIndex ()
	Returns an object that can calculate an index used in Cluster Validation. More...

_.DeepSee.extensions.clusters.CalinskiHarabasz	GetCalinskiHarabaszIndex (_.Library.Integer normalize)
	Returns an object that can calculate an index used in Cluster Validation. More...

	GetCentroid (_.Library.Integer k, z)
	Returns the coordinates for the centroid for a given cluster. More...

_.Library.Integer	GetCluster (_.Library.Integer point)
	Returns the cluster ordinal for a given point. More...

	GetClusterSize (_.Library.Integer k)
	Returns the number of data points assigned to a given cluster. More...

_.Library.Integer	GetCost (_.Library.Integer i, _.Library.Integer j)
	Returns the dissimilarity measure as used by this clustering algorithm. More...

_.Library.Integer	GetCount ()
	Returns the number of all data points in the model.

_.Library.Integer	GetDimensions ()
	Returns the dimensionality of the model.

_.Library.String	GetId (_.Library.Integer i)
	Returns the unque Id of the point with the ordinal number specified by i. More...

_.Library.Integer	GetNumberOfClusters ()
	Returns the number of clusters in the model.

_.DeepSee.extensions.clusters.PearsonGamma	GetPearsonGammaIndex ()
	Returns an object that can calculate an index used in Cluster Validation. More...

	GlobalCentroid (z)
	Returns the coordinates for the centroid for the whole dataset. More...

_.Library.Boolean	IsPrepared ()
	Checks whether the model is ready for an analysis to be executed. More...

_.Library.Double	RelativeClusterCost (_.Library.Integer k, _.Library.Integer m)
	Returns the realtive cost of a given cluster relative to a medoid point m. More...

	Reset ()
	Kills all the data associated with this model.

_.Library.Status	SetData (_.Library.IResultSet rs, _.Library.Integer dim, _.Library.Double nullReplacement)
	Sets the data to be associated with this model. More...

	iterateCluster (_.Library.Integer k, _.Library.Integer i, _.Library.String id, coordinates)
	Iterates over all the data points assigned to a given cluster. More...

	printAll ()
	Convenience method. More...

	printCluster (_.Library.Integer k)
	Convenience method. More...

Public Member Functions inherited from RegisteredObject
_.Library.Status	OnAddToSaveSet (_.Library.Integer depth, _.Library.Integer insert, _.Library.Integer callcount)
	This callback method is invoked when the current object is added to the SaveSet,. More...

_.Library.Status	OnClose ()
	This callback method is invoked by the <METHOD>Close</METHOD> method to. More...

_.Library.Status	OnConstructClone (_.Library.RegisteredObject object, _.Library.Boolean deep, _.Library.String cloned)
	This callback method is invoked by the <METHOD>ConstructClone</METHOD> method to. More...

_.Library.Status	OnNew ()
	This callback method is invoked by the <METHOD>New</METHOD> method to. More...

_.Library.Status	OnValidateObject ()
	This callback method is invoked by the <METHOD>ValidateObject</METHOD> method to. More...

Static Public Member Functions
_.Library.Status	Delete (_.Library.String dataset)
	Deletes a model for a dataset with the name given by dataset argument.

_.Library.Boolean	Exists (_.Library.String dataset)
	Checks whether a model for a dataset with the name given by dataset argument already exists.

Public Attributes
	DSName
	More...

	Dim
	More...

	Normalize
	Whether to normalize distance across multiple dimensions. More...

	P
	The power to use in calculation of dissimilarity. More...

	Verbose
	More...

Additional Inherited Members
Static Public Attributes inherited from RegisteredObject
	CAPTION = None
	Optional name used by the Form Wizard for a class when generating forms. More...

	JAVATYPE = None
	The Java type to be used when exported.

	PROPERTYVALIDATION = None
	This parameter controls the default validation behavior for the object. More...

Detailed Description

This class provides a base class for implementation for different Cluster Analysis algorithms.

It defines storage for clustering models and provides methods to retrieve information about data and clustering.

Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics.

By Default model data is stored in ^IRIS.Temp globals.

Member Function Documentation

◆ ById()

_.Library.Integer ById ( _.Library.RawString id )

Returns the ordinal number of the point with the given ID id.

The unique id must correspond to the one assigned in <METHOD>SetData</METHOD>() method

◆ Distance()

_.Library.Double Distance	(	_.Library.Integer	i,
		_.Library.Integer	j,
		_.Library.Double	p,
		_.Library.Boolean	normalize
	)

Returns the dissimilarity measure between two data points of the model.

The method takes 4 arguments:

i, j - Ordinal number of the data points in the model
p - Optional, if specified the power for a Minkowski distance. Default is Euclidean distance (p=2). Specify 1 for Manhattan Distance or 100 for Chebyshev distance (max between coordinates).
normalize - whether to normalize coordinates by their variances

◆ Distance1()

_.Library.Double Distance1	(	_.Library.Integer	i,
			z,
		_.Library.Double	p,
		_.Library.Boolean	normalize
	)

Returns the dissimilarity measure between a data points of the model and a point with given coordinates.

The method takes 4 arguments:

i - The ordinal number of the data point in the model
z - The multidimensional coordinates of the second point: z(1), z(2), ..., z(dim)
p - Optional, if specified the power for a Minkowski distance. Default is Euclidean distance (p=2). Specify 1 for Manhattan Distance or 100 for Chebyshev distance (max between coordinates).
normalize - whether to normalize coordinates by their variances

◆ Distance12()

_.Library.Double Distance12	(		z1,
			z2,
		_.Library.Double	p,
		_.Library.Boolean	normalize
	)

Returns the dissimilarity measure between two points with given coordinates.

The method takes 4 arguments:

z1, z2 - The multidimensional coordinates of the points: z1(1), z1(2), ..., z1(dim)
p - Optional, if specified the power for a Minkowski distance. Default is Euclidean distance (p=2). Specify 1 for Manhattan Distance or 100 for Chebyshev distance (max between coordinates).
normalize - whether to normalize coordinates by their variances

◆ GetASWIndex()

_.DeepSee.extensions.clusters.ASW GetASWIndex ( )

Returns an object that can calculate an index used in Cluster Validation.

and determining the optimal number of clusters. This method returns Average Silhouette Width index.

◆ GetCalinskiHarabaszIndex()

_.DeepSee.extensions.clusters.CalinskiHarabasz GetCalinskiHarabaszIndex ( _.Library.Integer normalize )

Returns an object that can calculate an index used in Cluster Validation.

and determining the optimal number of clusters. This method returns Calinski-Harabasz index.

◆ GetCentroid()

GetCentroid	(	_.Library.Integer	k,
			z
	)

Returns the coordinates for the centroid for a given cluster.

Cluster is identified by its ordinal number k.
Coordinates are returned as multidimensional value: z(1), z(2), ..., z(dim)

◆ GetCluster()

_.Library.Integer GetCluster ( _.Library.Integer point )

Returns the cluster ordinal for a given point.

Point is identified by its ordinal number.

◆ GetClusterSize()

GetClusterSize ( _.Library.Integer k )

Returns the number of data points assigned to a given cluster.

Cluster is identified by its ordinal number k.

◆ GetCost()

_.Library.Integer GetCost	(	_.Library.Integer	i,
		_.Library.Integer	j
	)

Returns the dissimilarity measure as used by this clustering algorithm.

between two data points of the model. Points are identified by their ordinal numbers.

◆ GetId()

_.Library.String GetId ( _.Library.Integer i )

Returns the unque Id of the point with the ordinal number specified by i.

The unique Id is as has been assigned in <METHOD>SetData</METHOD>() method

◆ GetPearsonGammaIndex()

_.DeepSee.extensions.clusters.PearsonGamma GetPearsonGammaIndex ( )

Returns an object that can calculate an index used in Cluster Validation.

and determining the optimal number of clusters. This method returns Pearson-Gamma index which is a correlation coefficient between distance between two points and a binary function whether they belong to the same cluster. This index is useful when clustering is used for dimension reduction i.e. the process of reducing the number of random variables under consideration

◆ GlobalCentroid()

GlobalCentroid ( z )

Returns the coordinates for the centroid for the whole dataset.

Coordinates are returned as multidimensional value: z(1), z(2), ..., z(dim)

◆ IsPrepared()

_.Library.Boolean IsPrepared ( )

Checks whether the model is ready for an analysis to be executed.

This is dependent on a

specific algorithm and therefore this method is overriden by subclasses.

Reimplemented in PAM, DissimilarityModel, and CLARA.

◆ RelativeClusterCost()

_.Library.Double RelativeClusterCost	(	_.Library.Integer	k,
		_.Library.Integer	m
	)

Returns the realtive cost of a given cluster relative to a medoid point m.

Cluster is identified by its ordinal number k. Point m is identified by its ordinal number.

◆ SetData()

_.Library.Status SetData	(	_.Library.IResultSet	rs,
		_.Library.Integer	dim,
		_.Library.Double	nullReplacement
	)

Sets the data to be associated with this model.

The method takes 3 arguments:

rs - is a result set that provides the data. The first column returned by the result set is assumed to be a unique Id of teh record. It is not used in any clustering algorithms but can be retrieved by the application to identify the record. It can be a database ID or any other value that makes sense to the application. Other columns provide numerical values for the coordinates of the record that are used by clustering algorithms. Result Set must contain at least dim + 1 columns.
dim - The dimensionality of the model, i.e. the number of the coordinates consumed by clustering algorithm.
nullReplacement - Optional, of specified this is a numeric replacement for empty values.

◆ iterateCluster()

iterateCluster	(	_.Library.Integer	k,
		_.Library.Integer	i,
		_.Library.String	id,
			coordinates
	)

Iterates over all the data points assigned to a given cluster.

Cluster is identified by its ordinal number k

◆ printAll()

printAll ( )

Convenience method.

Writes all data points in the dataset to the default output device.

◆ printCluster()

printCluster ( _.Library.Integer k )

Convenience method.

Writes all data points assigned to a given cluster to the default output device. Cluster is identified by its ordinal number k

Member Data Documentation

◆ DSName

DSName

◆ Dim

Dim

◆ Normalize

Normalize

Whether to normalize distance across multiple dimensions.

If set to 1 (default) then

distance is normalized by variances.

◆ P

P

The power to use in calculation of dissimilarity.

Default is Euclidean distance (P=2).

Specify 1 for Manhattan Distance or 100 for Chebyshev distance (max between coordinates).

◆ Verbose

Verbose

Public Member Functions

Static Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ ById()

◆ Distance()

◆ Distance1()

◆ Distance12()

◆ GetASWIndex()

◆ GetCalinskiHarabaszIndex()

◆ GetCentroid()

◆ GetCluster()

◆ GetClusterSize()

◆ GetCost()

◆ GetId()

◆ GetPearsonGammaIndex()

◆ GlobalCentroid()

◆ IsPrepared()

◆ RelativeClusterCost()

◆ SetData()

◆ iterateCluster()

◆ printAll()

◆ printCluster()

Member Data Documentation

◆ DSName

◆ Dim

◆ Normalize

◆ P

◆ Verbose