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user88
user88

Self organizing maps vs. kernel kmeansk-means

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel kmeansk-means to do the job. What are the pros and cons of each classifier for this task? Am-I missing others clustering algorithms that could be performant in this case?

Self organizing maps vs kernel kmeans

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel kmeans to do the job. What are the pros and cons of each classifier for this task? Am-I missing others clustering algorithms that could be performant in this case?

Self organizing maps vs. kernel k-means

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel k-means to do the job. What are the pros and cons of each classifier for this task? Am-I missing others clustering algorithms that could be performant in this case?

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WAF
WAF
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Self organizing maps vs kernel kmeans

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel kmeans to do the job. What are the pros and cons of each classifier for this task? Am-I missing others clustering algorithms that could be performant in this case?