# Tagged Questions

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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### K-means algorithm's EM “Maximization” step

I'm a software engineer and am trying to understand how Lloyd's K-Means algorithm fits into the general framework of the Expectation-Maximization (EM) algorithm. I previously read the question ...
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### Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions

I'm attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources. http://www2007.org/papers/paper570.pdf ...
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### Variables involved in kNNdistplot (dbscan package) in R

I have a time-series of a feature(metric) for 4 different servers each of length 2000. I want to use dbscan algorithm to figure out if all 4 machines fall in the same cluster or not using dbcscan on ...
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### k-means clustering minimizes conditional variance

I keep reading that K-means clustering "finds cluster centers that minimize conditional variance (good representation of data)". I understand conceptually how K-means clustering works, but please ...
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### Interpretation of the log likelihood in clustering techniques

Can Someone explain me how to interpret the log likelihood measure when evaluating clustering techniques? Let's say I am using Gaussian Mixture with Expectation Maximization, and I want to choose ...
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### Comparing kmeans cluster

I have 150 images, 15 each of 10 different people. So basically I know which image should belong together, if clustered. These images are of 73 dimensions (feature-vector) and I clustered them into ...
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### What Algorithm to cluster web user sessions without knowing the number of clusters?

I created user sessions from server log data. Based on the URLs I categorized each server request according to the respective page content (e.g. topic_1 = main page, topic_2 = team members, etc.). The ...
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### How would one use KDE as a one 1D clustering method?

I need to cluster a simple univariate data set into a preset number of clusters. Technically it would closer to binning or sorting the data since it is only 1D, but my boss is calling it clustering, ...
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### When would I use EM instead of k-means?

When would I want to assign cluster probabilities to patterns instead of hard assignments to clusters? Can someone elaborate?
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### Is there a difference between 1D Mean Shift and KDE for clustering 1 d data?

I need to cluster (or group) large one dimensional data sets into a set of fixed bins. I started out using K-means, but I want to look into other approaches. Two that I have found are Mean Shift and ...
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### Can I conduct multilevel analysis with two aggregated data sources?

I have two data sources with completely different specific individuals. One contains individuals answering whether or not they have watched the movie "Lego", and the other date source contains ...
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### Assigning new observations to already created clusters [duplicate]

I have created 6 segments in SPSS using the Two-Step Clustering approach based on about 2600 observations. I have now collected 1000 new observations and would like to be able to assign these new ...
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### Clustering and A/B testing

My question is the following: Let's imagine I've defined clusters in my data (different segments of customers) and I run an A/B test. Can I compare the performances of the different clusters on the ...
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### Cluster Component Coefficient Calculation for data in Rows

I have my data in the form such as: ...