HierClusterer
Cluster several vectors/points using hierarchical clustering. Start with each point forming its own cluster and merge clusters until there are only 'k'.
Value parameters
- k
-
stop when the number of clusters equals k
- x
-
the vectors/points to be clustered stored as rows of a matrix
Attributes
- Graph
-
- Supertypes
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 y, determine which cluster it belongs to.
Given a new point/vector y, determine which cluster it belongs 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
Iteratively merge clusters until the number of clusters equals 'k'.
Iteratively merge clusters until the number of clusters equals 'k'.
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