KMeansClusterer
The KMeansClusterer class cluster several vectors/points using k-means clustering. Randomly assign points to k clusters (primary technique). Iteratively, reassign each point to the cluster containing the closest centroid. Stop when there are no changes to the clusters.
Value parameters
- flags
-
the array of flags used to adjust the algorithm default: no post processing, no immediate return upon change
- k
-
the number of clusters to make
- x
-
the vectors/points to be clustered stored as rows of a matrix
Attributes
- See also
- Graph
-
- Supertypes
- Known subtypes
Members list
Value members
Concrete methods
Return the centroids. Should only be called after train.
Return the centroids. Should only be called after train.
Attributes
Given a new point/vector z, determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
Given a new point/vector z, determine which cluster it belongs to, i.e., the cluster whose centroid it is closest to.
Value parameters
- z
-
the vector to classify
Attributes
Return the cluster assignment vector. Should only be called after train.
Return the cluster assignment vector. Should only be called after train.
Attributes
Return the sizes of the centroids. Should only be called after train.
Return the sizes of the centroids. Should only be called after train.
Attributes
Show the state of the algorithm at iteration l.
Show the state of the algorithm at iteration l.
Value parameters
- l
-
the current iteration
Attributes
Iteratively recompute clusters until the assignment of points does not change. Initialize by randomly assigning points to k clusters.
Iteratively recompute clusters until the assignment of points does not change. Initialize by randomly assigning points to k clusters.
Attributes
Inherited methods
Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.
Calculate the centroids based on current assignment of points to clusters and update the 'cent' matrix that stores the centroids in its rows.
Value parameters
- cent
-
the matrix holding the centroids in its rows
- sz
-
the sizes of the clusters (number of points)
- to_c
-
the cluster assignment array
- x
-
the data matrix holding the points {x_i = x(i)} in its rows
Attributes
- Inherited from:
- Clusterer
Check to see if the sum of squared errors is optimum.
Check to see if the sum of squared errors is optimum.
Value parameters
- opt
-
the known (from human/oracle) optimum
- to_c
-
the cluster assignments
- x
-
the data matrix holding the points
Attributes
- Inherited from:
- Clusterer
Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'
Compute the distances between vector/point 'u' and the points stored as rows in matrix 'cn'
Value parameters
- cn
-
the matrix holding several centroids
- kc_
-
the number of centroids so far
- u
-
the given vector/point (u = x_i)
Attributes
- Inherited from:
- Clusterer
Return whether the centroids have been initialized.
Return the name of the 'c'-th cluster.
Return the name of the 'c'-th cluster.
Value parameters
- c
-
the c-th cluster
Attributes
- Inherited from:
- Clusterer
Set the names for the clusters.
Set the names for the clusters.
Value parameters
- nm
-
the array of names
Attributes
- Inherited from:
- Clusterer
Set the random stream to 's'. Method must be called in implemeting classes before creating any random generators.
Set the random stream to 's'. Method must be called in implemeting classes before creating any random generators.
Value parameters
- s
-
the new value for the random number stream
Attributes
- Inherited from:
- Clusterer
Compute the sum of squared errors from the points in cluster 'c' to the cluster's centroid.
Compute the sum of squared errors from the points in cluster 'c' to the cluster's centroid.
Value parameters
- c
-
the current cluster
- to_c
-
the cluster assignments
- x
-
the data matrix holding the points
Attributes
- Inherited from:
- Clusterer
Compute the sum of squared errors within all clusters, where error is indicated by e.g., the distance from a point to its centroid.
Compute the sum of squared errors within all clusters, where error is indicated by e.g., the distance from a point to its centroid.
Value parameters
- to_c
-
the cluster assignments
- x
-
the data matrix holding the points
Attributes
- Inherited from:
- Clusterer