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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.]
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Cluster points generated by mixtures of linear functions
I have a data set of N points, n X variables plus Y variable.
$$
(x^{(i)}_1,...,x^{(i)}_n,Y^{(i)}),\,\,\,\,i = 1,...,N
$$
generated by a mixture of $k$ linear dependencies; with this, I mean that ther …
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2
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Reference for agglomerative clustering poor performance
Agglomerative clustering is known to have poor performance on mid-big size datasets in terms of memory and speed. …
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answer
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Comparing a clustering algorithm partition to a "ground truth" one
If I feed a clustering algorithm with $X$, asking for $k$ clusters I would like to obtain a partition of the samples of $X$ that is the same of that induced by $y$, that is $P$. … I want to compare the partition generated by the clustering algorithm with the ground-truth partition $P$. …
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K-means with high dimensional data [duplicate]
I read in many places that k-means clustering algorithm does not perform well when dealing with multidimensional binary data (so vectors whose entries are zero or one). …