Detailed Description

KMeans clustering, partitions the data into k (a-priori specified) clusters.

It minimizes

$\sum_{i=1}^k\sum_{x_j\in S_i} (x_j-\mu_i)^2$

where $\mu_i$ are the cluster centers and $S_i,\;i=1,\dots,k$ are the index sets of the clusters.

Beware that this algorithm obtains only a local optimum.

cf. http://en.wikipedia.org/wiki/K-means_algorithm

Definition at line 39 of file KMeans.h.

Inheritance diagram for CKMeans:

[legend]

List of all members.

Public Member Functions
	CKMeans ()
	CKMeans (int32_t k, CDistance *d)
virtual	~CKMeans ()
virtual EClassifierType	get_classifier_type ()
virtual bool	load (FILE *srcfile)
virtual bool	save (FILE *dstfile)
void	set_k (int32_t p_k)
int32_t	get_k ()
void	set_max_iter (int32_t iter)
float64_t	get_max_iter ()
SGVector< float64_t >	get_radiuses ()
SGMatrix< float64_t >	get_cluster_centers ()
int32_t	get_dimensions ()
virtual const char *	get_name () const
Protected Member Functions
void	clustknb (bool use_old_mus, float64_t *mus_start)
virtual bool	train_machine (CFeatures *data=NULL)
virtual void	store_model_features ()
Protected Attributes
int32_t	max_iter
	maximum number of iterations
int32_t	k
	the k parameter in KMeans
int32_t	dimensions
	number of dimensions
SGVector< float64_t >	R
	radi of the clusters (size k)

Constructor & Destructor Documentation

CKMeans ( )

default constructor

Definition at line 29 of file KMeans.cpp.

CKMeans	(	int32_t	k,
		CDistance *	d
	)

constructor

Parameters:

	k	parameter k
	d	distance

Definition at line 35 of file KMeans.cpp.

~CKMeans ( ) [virtual]

Definition at line 43 of file KMeans.cpp.

Member Function Documentation

void clustknb	(	bool	use_old_mus,
		float64_t *	mus_start
	)			`[protected]`

clustknb

Parameters:

	use_old_mus	if old mus shall be used
	mus_start	mus start

replace rhs feature vectors

set rhs to mus_start

update rhs

Definition at line 134 of file KMeans.cpp.

virtual EClassifierType get_classifier_type ( ) [virtual]

get classifier type

Returns:: classifier type KMEANS

Reimplemented from CMachine.

Definition at line 57 of file KMeans.h.

SGMatrix<float64_t> get_cluster_centers ( )

get centers

Definition at line 119 of file KMeans.h.

int32_t get_dimensions ( )

get dimensions

Returns:: number of dimensions

Definition at line 136 of file KMeans.h.

int32_t get_k ( )

get k

Returns:: the parameter k

Definition at line 87 of file KMeans.h.

float64_t get_max_iter ( )

get maximum number of iterations

Returns:: maximum number of iterations

Definition at line 106 of file KMeans.h.

virtual const char* get_name ( void ) const [virtual]

Returns:: object name

Reimplemented from CDistanceMachine.

Definition at line 142 of file KMeans.h.

SGVector<float64_t> get_radiuses ( )

get radiuses

Definition at line 114 of file KMeans.h.

bool load ( FILE * srcfile ) [virtual]

load distance machine from file

Parameters:

srcfile

file to load from

Returns:: if loading was successful

Reimplemented from CMachine.

Definition at line 73 of file KMeans.cpp.

bool save ( FILE * dstfile ) [virtual]

save distance machine to file

Parameters:

dstfile

file to save to

Returns:: if saving was successful

Reimplemented from CMachine.

Definition at line 80 of file KMeans.cpp.

void set_k ( int32_t p_k )

set k

Parameters:

p_k

new k

Definition at line 77 of file KMeans.h.

void set_max_iter ( int32_t iter )

set maximum number of iterations

Parameters:

iter

the new maximum

Definition at line 96 of file KMeans.h.

void store_model_features ( ) [protected, virtual]

Ensures cluster centers are in lhs of underlying distance

Reimplemented from CDistanceMachine.

Definition at line 419 of file KMeans.cpp.

bool train_machine ( CFeatures * data = NULL ) [protected, virtual]

train k-means

Parameters:

data

training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)

Returns:: whether training was successful

Reimplemented from CMachine.

Definition at line 48 of file KMeans.cpp.

Member Data Documentation

int32_t dimensions [protected]

number of dimensions

Definition at line 176 of file KMeans.h.

int32_t k [protected]

the k parameter in KMeans

Definition at line 173 of file KMeans.h.

int32_t max_iter [protected]

maximum number of iterations

Definition at line 170 of file KMeans.h.

SGVector<float64_t> R [protected]

radi of the clusters (size k)

Definition at line 179 of file KMeans.h.

The documentation for this class was generated from the following files:

CKMeans Class Reference

Detailed Description

Public Member Functions

Protected Member Functions

Protected Attributes

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation