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Thoughts on SSCL Algorithm
Shiliang Sun, Naveed, Yin Wang, Jinjin Cai
2005-5-30
Group Discussion Summary on
SSCL Algorithm
1
Outlines
 Advantages and Disadvantages of SSCL
 A Question about SSCL Procedure
 How ‘Enough’ can be Quantified and
Managed
 A Spawning Mechanism Requiring
Reproduction
 Possible Probability Modeling Approaches
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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Advantages and Disadvantages
 Advantages
Overcome two critical problems in clustering:
 The difficulty in determining the number of clusters;
 The sensitivity to prototype initialization.
 Disadvantages
 Sensitive on the sampling scheme and the
distribution of data sets. In some cases, it takes a
long time to converge.
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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A Question about SSCL Procedure
 Initialization:
 ….
Set APV at a random location far from
prototype.
 Question:
How far? To what extent does this
configuration influence the finial clustering
result?
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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Quantification and Management on
‘Enough’
 In human society there can never be enough in
anything humans are competing for.
 A mechanism:
 Define the highest value based on the average
line, e.g. Average(1+20%). The variance is to
encourage good winner.
 Punish or balance among winners, e.g. discount if
more than Average(1+20%), and tax law in the
human society.
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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A Spawning Mechanism Requiring
Reproduction
 SSCL starts from a single prototype and uses sexless spawning.
 There is no merge procedure in SSCL algorithm.
Maybe it would be more sense to add the merge
step. If some clusters split have similar attributes,
of course they should merge together to form one
cluster.
 Does the case happen? That is, more clusters are
found than the true number of clusters.
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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Possible Probability Modeling
Approaches
 Since SSCL is sensitive on the sampling scheme
and the distribution of data sets, we think the more
possible utility of probability modeling is to deal
with this scenario.
 As a byproduct, maybe more theoretical
accomplishments would be obtained.
 How to use the distribution information hidden in
the given data is very interesting. For instance,
mean-shift clustering approach is solely based on
probability density distribution. Perhaps, using
mean-shift to find DPVs would be more effective.
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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Thanks!
 Contact Information of Shiliang SUN:
[email protected]
 State Key Lab. of Pattern Recognition and
Intelligent Systems, Department of Automation,
Tsinghua University.
2005-5-30
Group Discussion Summary on
SSCL Algorithm
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