stream <- DSD_Gaussians(k=3, d=2)
dstream <- DSC_DStream(gridsize=0.05, Cm=1.5)
update(dstream, stream, 500)
plot(dstream, stream)
# Evaluate micro-clusters
# Note: we use here only n=500 points for evaluation to speed up execution
evaluate(dstream, stream, measure=c("numMicro","numMacro","purity","crand", "SSQ"),
n=100)
# DStream also provides macro clusters. Evaluate macro clusters with type="macro"
plot(dstream, stream, type="macro")
evaluate(dstream, stream, type ="macro",
measure=c("numMicro","numMacro","purity","crand", "SSQ"), n=100)
# Points are by default assigned to the closest micro clusters for evalution.
# However, points can also be assigned to the closest macro-cluster using
# assign="macro".
evaluate(dstream, stream, type ="macro", assign="macro",
measure=c("numMicro","numMacro","purity","crand", "SSQ"), n=100)
# Evaluate an evolving data stream
stream <- DSD_Benchmark(1)
dstream <- DSC_DStream(gridsize=0.05, lambda=0.1)
evaluate_cluster(dstream, stream, type="macro", assign="micro",
measure=c("numMicro","numMacro","purity","crand"),
n=600, horizon=100)
## Not run:
# # animate the clustering process
# reset_stream(stream)
# dstream <- DSC_DStream(gridsize=0.05, lambda=0.1)
# animate_cluster(dstream, stream, horizon=100, n=5000,
# measure=c("crand"), type="macro", assign="micro",
# plot.args = list(type="both", xlim=c(0,1), ylim=c(0,1)))
# ## End(Not run)
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