#KMeans Quantizer

##Description The KMeansQuantizer module quantizes an N-dimensional input vector to a 1-dimensional discrete value. This quantized value will be between [0 K-1], where K is the number of clusters used to create the quantization model. The KMeansQuantizer can be useful as input to classifiers such as Hidden Markov Models, which require a discrete integer as an input as opposed to a continuous floating point N-dimensional vector.

Before you use the KMeansQuantizer, you need to train a quantization model. To do this, you select the number of clusters you want your quantizer to have and then give it any training data in the following formats:

• ClassificationData
• TimeSeriesClassificationData
• TimeSeriesClassificationDataStream
• UnlabelledData
• MatrixFloat

The KMeansQuantizer class is part of the Feature Extraction Modules.

##Example KMeansQuantizer Example