kMeansMiniBatch (Stream Processor)

Performs K-Means clustering on a streaming data set. Data points can be of any dimension and the dimensionality is calculated from number of parameters. All data points to be processed in a single query should be of the same dimensionality. The Euclidean distance is taken as the distance metric. The algorithm resembles mini-batch K-Means. (refer Web-Scale K-Means Clustering by D.Sculley, Google, Inc.).

Syntax

streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <INT> maximum.iterations, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <INT> maximum.iterations, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <INT> maximum.iterations, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <INT> maximum.iterations, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)

Query Parameters

Name	Description	Default Value	Possible Data Types	Optional	Dynamic
no.of.clusters	The assumed number of natural clusters in the data set.		INT	No	No
decay.rate	this is the decay rate of old data compared to new data. Value of this will be in [0,1]. 0 means only old data used and1 will mean that only new data is used	0.01	DOUBLE	Yes	No
maximum.iterations	Number of iterations, the process iterates until the number of maximum iterations is reached or the centroids do not change	50	INT	Yes	No
no.of.events.to.retrain	number of events to recalculate cluster centers.	20	INT	Yes	No
model.feature	This is a variable length argument. Depending on the dimensionality of data points we will receive coordinates as features along each axis.		DOUBLE FLOAT INT LONG	No	Yes

Extra Return Attributes

Name	Description	Possible Types
euclideanDistanceToClosestCentroid	Represents the Euclidean distance between the current data point and the closest centroid.	DOUBLE
closestCentroidCoordinate	This is a variable length attribute. Depending on the dimensionality(d) we will return closestCentroidCoordinate1 to closestCentroidCoordinated which are the d dimensional coordinates of the closest centroid from the model to the current event. This is the prediction result and this represents the cluster towhich the current event belongs to.	DOUBLE

Example 1

CREATE STREAM InputStream (x double, y double);
@info(name = 'query1')
insert into OutputStream
select closestCentroidCoordinate1, closestCentroidCoordinate2, x, y    
from InputStream#streamingml:kMeansMiniBatch(2, 0.2, 10, 20, x, y);

This is an example where user gives all three hyper parameters. first 20 events will be consumed to build the model and from the 21st event prediction would start.

Example 2

CREATE STREAM InputStream (x double, y double);
@info(name = 'query1')
insert into OutputStream
select closestCentroidCoordinate1, closestCentroidCoordinate2, x, y    
from InputStream#streamingml:kMeansMiniBatch(2, x, y);

This is an example where user has not specified hyper parameters, so default values are used.