# Questions tagged [clustering]

For questions related to clustering (a usual unsupervised learning technique).

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### How to detect patterns in salary distribution if we are suspecting malicious distribution based on employee's region?

We are having suspects in salary distribution in our organisation due to employee's region. The data we have is as the following: ...
49 views

### Grey Wolf Optimization - Issue with Dimension [closed]

I'm trying to use the grey wolf optimization (GWO) for texts clustering. I used this code, https://github.com/7ossam81/EvoloPy-NN/blob/master/selector.py I tried using the dimension 30 for the GWO as ...
15 views

### Clustering of very high dimensional data and large number of examples without losing info in dimensions

I'm trying to get a grasp on scalability of clustering algorithms, and have a toy example in mind. Let's say I have around a million or so songs from $50$ genres. Each song has characteristics - some ...
90 views

### Is unsupervised learning a branch of AI?

From Artificial Intelligence: A Modern Approach, a book by Stuart Russell and Peter Norvig, this is the definition of AI: We define AI as the study of agents that receive percepts from the ...
20 views

### How can I cluster based on the complementary categories?

K-means tries to find centroid and then clusters around the centroids. But what if we want to cluster based on the complement? For example, suppose we have a group of animals and we want to cluster ...
37 views

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

According to my lecture, Fuzzy c-Means tries to minimize the following objective function: $$J(X,B,U)=\sum_{i=1}^c\sum_{j=1}^n u_{ij}^w \, d^2(\vec{\beta_i},\vec{x_j})$$ where $X$ are the data ...
113 views

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

I need to cluster my points into unknown number of clusters, given the minimal Euclidean distance R between the two clusters. Any two clusters that are closer than this minimal distance should be ...
There are several (family of) algorithms that can be used to cluster a set of $d$-dimensional points: for example, k-means, k-medoids, hierarchical clustering (agglomerative or divisive). What is ...