stream (version 0.1-1)

DSC_tNN: Threshold Nearest Neighbor (tNN) Data Stream Clustering Algorithm

Description

Implements the tNN (threshold Nearest Neighbor) data stream algorithm.

Usage

DSC_tNN(r = 0.1, k = 0, alpha = 0, minweight = 0, lambda = 0.001, 
	decay_interval = 1000L, noise = 0.01, measure = "Euclidean", 
	macro = TRUE)

Arguments

r

The threshold in the nearest neighborhood algorithm.

k

The number of macro clusters to be returned if macro is true.

alpha

The minimum proportion of shared points between to clusters to warrant combining them.

minweight

The minimum number of weight a micro-cluster needs to have.

lambda

The lambda used in the fading function.

decay_interval

Fading is only called every decay_interval points.

noise

The amount of noise that should be removed while clustering.

measure

The measure used to calculate cluster proximity (see package proxy).

macro

A flag that indicates if the macro clusters should be computed.

Value

• An object of class DSC_tNN (subclass of DSC, DSC_R, DSC_Micro).

Details

The threshold Nearest Neighbor algorithm checks for each new data point in the incoming stream, if it is below the threshold value of dissimilarity value of any existing micro-clusters, and if so, merges the point with the micro-cluster. Otherwise, a new micro-cluster is created to accommodate the new data point.

Note: Although DSC_tNN is a micro clustering algoritm, macro clusters and weights are available.

References

M.H. Dunham, Y. Meng, J. Huang (2004): Extensible Markov Model, In: ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining, pp. 371-374.

M. Hahsler, M. H. Dunham (2010): rEMM: Extensible Markov Model for Data Stream Clustering in R, Journal of Statistical Software, 35(5), 1-31, URL http://www.jstatsoft.org/v35/i05/

See Also

DSC, DSC_Micro

Examples

# Cassini
dsd <- DSD_mlbenchGenerator("cassini")

# tNN has a built in micro and micro-clusterer
tnn <- DSC_tNN(r=.2, k=3, alpha=.08, lambda=0)
cluster(tnn, dsd, 500)

# see micro-clusters
nclusters(tnn)
head(get_centers(tnn))

# see macro-clusters
nclusters(tnn, type="macro")
get_centers(tnn, type="macro")


# see how micro and macro-clusters relate
# each microcluster has an entry with the macro-cluster id
# Note: unassigned micro-clusters (noise) have an NA
microToMacro(tnn)


# plot micro-clusters
plot(tnn, dsd)
# plot macro-clusters
plot(tnn, dsd, type="macro")

# evaluate first using macro and then using micro-clusters
evaluate(tnn, dsd, method="cRand")
evaluate(tnn, dsd, method="cRand", type="macro")
evaluate(tnn, dsd, method="cRand", type="macro", assign="macro")