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You can see this comparison table in sklearn, which gives some intuition about where and when each algorithm is successful:
It might be a good idea to try both and evaluate their accuracy, with an unsupervised clustering metric, like the silhouette score, to get an objective measure of their performance on a specific dataset.
Some other major differences ...
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DBSCAN will always mark noisy points according to epsilon and min_samples parameters, so there is no way to avoid that unless you have very compact and "well defined" clusters, what seems unlikely.The short answers will be to use another clustering algorithm such as gaussian mixture, k-means, or Birch
If your problem really needs you to use DBSCAN, ...
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To add to WBM great citation, you should use K-means over Agglomerative when your final objetive is to use the trained algorithm to make inference over new unseen observations.
I will try to illustrate this with an example:
Imagine you have 2 models kmeans and aggcls both have been trained on data that correspond to information of customers on an specific ...
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This happens if:
There is no clear way to separate the clusters, i.e. there is only one large group of instances and the instances which are far from this group are too far from each other to form their own cluster.
The linkage criterion plays a big role: this is more likely to happen with single linkage clustering , because as the main cluster grows it ...
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You can get a dendrogram from any hierarchical clustering method. The tricky thing here is how to compute the distances between the words. If efficiency is your main concern, I would consider using HDBSCAN clustering.
The Jaro-Winkler distance was originally designed for such tasks. There is an efficient implementation in the python Levenshtein package, but ...
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As you mention it might be solved via clustering, but given you need the top n to each other you can go as follows:
Assuming you have matrix X of nxm (n- batteries m- features/attributes of each one)
Define a distance metric (Euclidean, Mahalanobis, etc)
Calculate the distance between a battery j and all the other batteries i - j
Sort the top n distances ...
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That is often called nearest-neighbor search.
The most common methods require a distance metric. Given the features of battery pack, how close to each other are they?
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I don't know if there are other clustering methods which would work with this amount of data, but with K-means I would suggest this:
Run K-means with a varying number of instances picked randomly and study how much variation there is between the centroids depending on the data size (you can also study the variation across different random samples). I would ...
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