Space: Apache Mahout (https://cwiki.apache.org/confluence/display/MAHOUT)
Page: Spectral Clustering
(https://cwiki.apache.org/confluence/display/MAHOUT/Spectral+Clustering)
Edited by Shannon Quinn:
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Spectral clustering is a more powerful and specialized algorithm (compared to
K-means) which has significant use in photo editing, hence its name. Each
object to be clustered can initially be represented as an _n_\-dimensional
numeric vector, but the difference with this algorithm is that there must also
be some method for performing a comparison between each object and expressing
this comparison as a scalar.
This _n_ by _n_ comparison of all objects with all others forms the _affinity_
matrix, which can be intuitively thought of as a rough representation of an
underlying undirected, weighted, and fully-connected graph whose edges express
the relative relationships, or affinities, between each pair of objects in the
original data. This affinity matrix forms the basis from which the two spectral
clustering algorithms operate.
The equation by which the affinities are calculated can vary depending on the
user's circumstances; typically, the equation takes the form of:
exp( _d{_}{^}2^ / _c_ )
where _d_ is the Euclidean distance between a pair of points, and _c_ is a
scaling factor. _c_ is often calculated relative to a _k_\-neighborhood of
closest points to the current point; all other affinities are set to 0 outside
of the neighborhood. Again, this formula can vary depending on the situation
(e.g. a fully-connected graph would ignore the _k_\-neighborhood and calculate
affinities for all pairs of points).
h2. K-Means Spectral Clustering
h3. Overview
h3. Implementation
h2. Eigencuts Spectral Clustering
h3. Overview
h3. Implementation
h2. Quickstart
h2. Examples
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