Latent Block Model Predictor TODO

Labels / Tags

Predictor
Numerical vector

Principle

$K$MeansPredictor inherit from ClosestPrototypePredictor and follow the same logic in a more specific way. Here both queries and prototypes have share the type numerical vector. Result of a query will be the ClusterId of the closest $K$MeansModel prototype.

Scalability

When dissimilarity measure complexity is considered negligeable the computational complexity of a $K$MeansPredictor query is in O(K)

Where :

K is the number of prototype of the $K$MeansModel.

If there are $n$ queries, the complexity will be $O(K.n)$. Considering than in most of case K is neglieable compared to n, a collection of query will be executed in linear time.

Be sure to know the computational complexity of used dissimilarity measure.

Input

Single query

A numerical vector value.

Multiple queries, i.e collection of data observations

A collection of numerical vector value.

Parameters

1 : $K$`MeansModel`

KMeansModel contains the list of prototypes returned by KMeans algorithm.

Output

Single query

Returns the ClusterId of the closest KMeansModel prototype.

Multiple queries, i.e. a collection of queries of numerical vectors

Returns the HardClustering associates to input data.

Associate visualizations

HardClustering