Introduction to K-means Clustering
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Introduction to K-means Clustering
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Re: Introduction to K-means Clustering
#2See also self-organizing maps. They seem more robust. See e.g. [1].
[1] https://pdfs.semanticscholar.org/3ffe/8f8a7b0d00297e0cd74d20...
Re: Introduction to K-means Clustering
#3K-means is based on some quite wild assumptions - your data follows a specific case of the Gaussian distribution. Plus side is that the algorithm is relatively easy to understand and implement so it is a good starting point into clustering.
When you got this, then next step is probably to consider if the centroid is really a good representation of the distribution of your data. If you still believe that your data is about to follow some form of Gaussian distribution then K-means can be levelled up with the EM algorithm.
More over, if you are faced with some crazy multidimensional data then it may be fair first try to reduce the dimensionality of the data with the PCA or t-SNE algorithm or with combination of both. Or perhaps use autoencoders.
Re: Introduction to K-means Clustering
#4My favorite cluster algorithm comparison:
http://nbviewer.jupyter.org/github/scikit-learn-contrib/hdbs...
Re: Introduction to K-means Clustering
#5K-means is based on some quite wild assumptions - your data follows a specific case of the Gaussian distribution. Plus side is that the algorithm is relatively easy to understand and implement so it is a good starting point into clustering. When you got this, then next step is probably to consider if the centroid is really a good representation of the distribution of your data. If you still believe that your data is…
What about data that generally follows something more like a power law distribution? How would you cluster that?
Re: Introduction to K-means Clustering
#6K-means is based on some quite wild assumptions - your data follows a specific case of the Gaussian distribution. Plus side is that the algorithm is relatively easy to understand and implement so it is a good starting point into clustering. When you got this, then next step is probably to consider if the centroid is really a good representation of the distribution of your data. If you still believe that your data is…
What about data that generally follows something more like a power law distribution? How would you cluster that?
scikit-learn's materials (amazing in general) has a nice overview of their clustering methods and how they perform with some test data sets: http://scikit-learn.org/stable/modules/clustering.html
How would you even cluster a power law distribution? There's nothing that distinguishes in two or more groups?
Re: Introduction to K-means Clustering
#7K-means is based on some quite wild assumptions - your data follows a specific case of the Gaussian distribution. Plus side is that the algorithm is relatively easy to understand and implement so it is a good starting point into clustering. When you got this, then next step is probably to consider if the centroid is really a good representation of the distribution of your data. If you still believe that your data is…
What about data that generally follows something more like a power law distribution? How would you cluster that?
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Re: Introduction to K-means Clustering
#8I created a visualization of k-means for some people at my school a few months ago. I'll put it here for anyone who might be interested.
Re: Introduction to K-means Clustering
#9I wrote a JS implementation and tutorial on k-means a few years back [1], if anyone is looking for this in the browser. Great writeup, I appreciate the math focus!
[1] http://burakkanber.com/blog/machine-learning-k-means-cluster...
Re: Introduction to K-means Clustering
#10In my experiments K-Means clustering proved to be super useful to detect malicious activities, cyberattacks and all kind of suspicious behaviors without knowing any rules or patterns in advance.
Here's how K-Means with K=20 detected malicious activity within real world, live web server - just by selecting smallest clusters (bottom table shows specifics about what fallen into smallest clusters - all hits but the first one are attempt to exploit web application by some nefarious actors):
This is built using free version of Splunk with free machine learning toolkit addon.