kMeansIncremental (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 by a query should be of the same dimensionality. The Euclidean distance is taken as the distance metric. The algorithm resembles Sequential K-Means Clustering.

Syntax

streamingml:kMeansIncremental(<INT> no.of.clusters, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansIncremental(<INT> no.of.clusters, <DOUBLE> decay.rate, <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
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, closestCentroidCoordinate2,... 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 to which 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:kMeansIncremental(2, 0.2, x, y);

This is an example where user provides the decay rate. First two events will be used to initiate the model since the required number of clusters is specified as 2. After the first event itself 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:kMeansIncremental(2, x, y);

This is an example where the user doesn't give the decay rate, so the default value is used.