Seminar on Computational Learning and
Adaptation
K-Harmonic Means: A New Clustering Algorithm
Bin Zhang
Hewlett Packard Research Laboratory
Palo Alto, CA
bin_zhang2@hp.com
The most common algorithms for clustering, K-means (KM) and
expectation-maximization (EM), are highly sensitive to how cluster
centers are initialized. No good initialization method is known for
EM or KM. We propose a new algorithm, named K-Harmonic Means (KHM).
Experiments show that KHM is almost insensitive to how centers are
initialized, and often maximizes the KM objective function better
than KM itself. Instead of the minimum distance from a data point
to the centers, as used by KM, KHM uses the harmonic mean of the
distances from the data point to all centers.
This talk gives a unified mathematical and experimental analysis
of KM, EM, and KHM, revealing their similarities, differences,
advantages, and disadvantages, with visual animations of the
convergence of all three algorithms.
Date: Thurs., November 16
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Time: 4:15-5:30PM
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Place: Cordura 100
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