Clustering is a widely used knowledge discovery technique.
Large-scale clustering has received a lot of attention recently.
However, existing algorithms often do not scale with the size of
the data and the number of dimensions, or fail to find arbitrary
shapes of clusters or deal effectively with the presence of noise.
In this book a new clustering algorithm based on self-similarity
properties is discussed. Self-similarity is the property of being
invariant with respect to the scale used to look at the data set.
While fractals are self-similar at every scale, many data sets only
exhibit self-similarity over a range of scales. Self- similarity
can be measured using the fractal dimension. Our new clustering
algorithm called Fractal Clustering (FC) places points
incrementally in the cluster for which the change in the fractal
dimension after adding the point is the least, so points in the
same cluster have a great degree of self-similarity among them (and
much less self- similarity with respect to points in other
clusters). Two applications on projected clustering and tracking
deviation in evolving data sets are also discussed.
General
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