DSC_PreDeConStream: Density-Based Projected Clustering of Data Streams

In subspaceMOA: Interface to 'subspaceMOA'

Description

This function creates a DSC object that represents an instance of the PreDeConStream algorithm and can be used for stream clustering.

Usage

DSC_PreDeConStream(epsilonN = 0.7, beta = 0.3, muN = 10, muF = 3,
  lambda = 0.1, initPoints = 1000, tau = 2, kappa = 10, delta = 0.01,
  offline = 2, speed = 100)

Arguments

epsilonN	radius of each neighborhood
beta	control the effect of mu
muN	minimum number of points in microclusters
muF	minimum number of points in macroclusters
lambda	decaying parameter
initPoints	number of points to use for initialization
tau	number of maximal subspace dimensionality
kappa	parameter to define preference weighted vector
delta	defines the threshold for the variance
offline	offline multiplier for epsilon
speed	processing number of incoming points per time unit

Details

The PreDeConStream algorithm is a Density-Based algorithm for the projected clustering of data streams. To initially obtain a set of microclusters initPoints points are buffered and clustered using the PreDeCon algorithm. Then, microclusters are maintained by checking for each new point whether it falls within the radius of an existing microcluster, similar to DSC_DenStream. Microclusters are aged according to a decay paramter lambda. Macroclusters are also maintained throughout the run of the algorithm by updating the affected macroclusters, whenever a change in the microcluster structure has occured, using a component of the PreDeCon algorithm to do so.

Examples

dsc <- DSC_PreDeConStream()
dsd <- DSD_RandomRBFSubspaceGeneratorEvents()
update(dsc,dsd,1000)

subspaceMOA documentation built on May 29, 2017, 10:50 p.m.