5
votes
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 ...
- 41.1k
5
votes
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 ...
- 1,293
3
votes
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 ...
- 41.1k
3
votes
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 ...
- 216
2
votes
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 ...
- 5,397
2
votes
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
2
votes
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 ...
- 41.1k
2
votes
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 ...
- 5,397
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 ...
- 1,660
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 ...
- 2,379
1
vote
What does "Clustering features based on their values across objects rather than clustering objects ($X^T$ rather than$ X$)" mean?
This is a 'reversal' of the usual clustering approach. Normally you cluster objects, and you use their features to define similarity (as proximity in 'feature-space'). So you start off with a set of ...
- 5,397
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 ...
- 5,397
Only top scored, non community-wiki answers of a minimum length are eligible