# Tag Info

### What is graph clustering?

In graph clustering, we want to cluster the nodes of a given graph, such that nodes in the same cluster are highly connected (by edges) and nodes in different clusters are poorly or not connected at ...
• 37k
Accepted

### How to refine K-means clustering on a data set?

The usual parameters to adjust in a k-means: Number of clusters (recall many clusters can have same label). Distance definition (euclidean is the most basic, Gauss is an improvement) Selection of ...
• 1,282
Accepted

### How to compute the number of centroids for K-means clustering algorithm given minimal distance?

Yes, the silhouette method (which is implemented in sklearn as silhouette_score) is commonly used to assess the quality of ...
• 37k
Accepted

### Is there a machine learning algorithm to find similar sales patterns?

If I understand correctly you want to find companies with similar patterns to yours. I would start with measuring cosine similarity between your company and ...
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### Is there a machine learning algorithm to find similar sales patterns?

I would recommend a hierarchical cluster algorithm, after normalising your numbers into proportions. Then the clustering should be able to identify similar patterns. Depending at which level you make ...
• 5,252

### Is this dataset with only two features suitable for clustering with k-means?

One problem with clustering algorithms is that they will typically find you a solution, ie they will split your data set into clusters, but it will find you a structure even if there isn't one. Your ...
• 5,252
Accepted

### Can I do state space quantization using a KMeans-like algorithm instead of range buckets?

There is this paper Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients, presented in the Machine Learning for Health Workshop in NIPS 2017. Here is a quote ...
• 722
Accepted

### What is the borderline between unsupervised learning and regular algorithms?

Any algorithm that uses data (in some form) to improve some performance measure (aka objective function), or to find some function, can be considered a machine learning algorithm. See this answer for ...
• 37k
1 vote

### Why does k-means have more bias than spectral clustering and GMM?

I'm not an expert on clustering, but here's my take below. Note that this is only based on theoretical arguments, I haven't had enough clustering experience to say if this is generally true in ...
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1 vote

### What is the role of the 'fuzzifier' w in Fuzzy Clustering?

Its not required, you can have $m=1$, actually it can be any number $\geq 1$. Now the better question is why to have it? The answer is that it adds a smoothing effect. Lets look at it in each of ...
• 2,339
1 vote

### How to compute the number of centroids for K-means clustering algorithm given minimal distance?

If you look at Kaufman & Rousseeuw (1990), Finding Groups in Data, they describe an algorithm to evaluate the quality of clusters in agglomerative clustering. You run the clustering algorithm with ...
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