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Results tagged with hierarchical-clustering
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Hierarchical cluster analysis is a method of cluster analysis which builds, by steps, a hierarchy of clusters, a dendrogram. Most popular is agglomerative hierarchical clustering (HAC) which starts from individual objects and collects them into bigger and bigger clusters.
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Is there any effective way to tell difference between clusters
You can think of the cluster assigned to each point as a class label. This will let you address things the same way you would for a classification problem: "what are the features that distinguish thes …
- 33.9k
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Agglomerative clustering with must-link constraints
In the usual, uncontrained case, hierarchical agglomerative clustering starts with every point assigned to its own cluster. On each iteration, we select two clusters to merge, according to some chosen …
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What are some clustering algorithms in which I can define no of clusters I require?
A common approach with k-means is to run multiple times with different initializations, then pick the best. Some other options that let you pick the number of clusters: Gaussian mixture models, agglom …
- 33.9k
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Clustering sets of vectors
Hierarchical agglomerative clustering might work for you. It typically starts with each data point in its own cluster, then iteratively merges pairs of clusters to form larger and larger clusters. Sin …
- 33.9k
3
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Difference between languages (spoken)?
The simplest option might be to ignore distances between languages themselves. You could construct a vector for each neighborhood, where each element corresponds to a language, and the its value corre …
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Does it make sense to cluster based on Euclidean distances between rows of a cosine matrix?
It sounds like you're doing the following:
Given $n$ data points $\{X_1, ..., X_n\}$, calculate a pairwise cosine distance matrix $C$, where $C_{ij}$ is the cosine distance between points $i$ and $j …
- 33.9k