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Results tagged with clustering
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user 204731
Cluster analysis is the task of partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. [Clustered-standard-errors and/or cluster-samples should be tagged as such; do NOT use the "clustering" tag for them.]
4
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How to determine the best batch-size value for Mini Batch K-means algorithm?
I am working on a project where I apply k-means on severals datasets. These datasets may include up to several billion points. I would like to use mini batch k-means to save time. However, the mini ba …
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What are differences between K-means versions: Lloyd, Forgy, Macquen, Hartigan and other?
I'm looking for the (perhaps brief) explanation of the main differences between the different K-means clustering procedures, such as between Lloyd, Forgy, MacQueen and Hartigan, and possibly other versions …
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1
vote
1
answer
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What is the technique to measure the performance of the methods clustering?
I don't see how to evaluate the performance of the methods in order to choose the best method clustering. Because a clustering method is not the best for each measure. … Is there a way or a technique to identify the best clustering method. …
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1
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1
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285
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How can we compute the difference between two silhouette scores for the same dataset?
Given a dataset X on which I applied k-means and I computed the Silhouette Index score. I consider this score as the truth. I applied again k-means on X and I computed the Silhouette Index score. My q …
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0
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2
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How k-means computes cluster centroids differently for each distance metric?
K-means computes cluster centroids differently for each distance metric. I don't know why the way of computing the centroid is dependent of the distance measure.
I don't know how we compute the cent …
- 333
4
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1
answer
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which metrics are suitable for density-based clustering validation?
I'm working on a project where I use several clustering methods, mainly density based ones such as hdbscan, optics... … I'm looking for a metric to evaluate clustering results that takes into account outliers and different forms of clusters. …
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How to estimate the leafsize of the kd-tree?
The kd-tree implementation proposed by the scipy python libray asks for the value of the leafsize parameter that is to say the maximum number of points a node can hold. It is by default set to 10.
Ar …
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