# Tagged Questions

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### How to find the number of clusters in 1d data and the mean of each

We have a list of prices and need to find both the number of clusters (or intervals) and the mean price of each cluster (or interval). The only constraint is that we want cluster means to be at least ...
90 views

### Machine learning algorithms/approaches for class recommendations?

I am asking a theoretical question about machine learning in terms of clustering. Is it possible, given a set of data of classes that students have taken in a semester to recommend additional classes ...
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### Fractionation clustering

I have a question regarding fractionation clustering algorithm introduced in http://www.jopedersen.com/Publications/cutting92scattergather.pdf. It's described so vaguely that I'm not sure if I got it ...
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### Difficulty in understanding a vector quantization algorithm

Neural gas for vector quantizationpaper explains a technique for symbolizing or quantizing data. Algorithm presents the algorithm in Section 4. An application in EEG data symbolization is presented ...
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### Difference between standard and spherical k-means algorithms

I would like to understand, what is the major implementation difference between standard and spherical k-means clustering algorithms. In each step, k-means computes distances between element vectors ...
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### Clustering-based Identification

I am trying to find a way to merge data that correspond to users and have the form $user_i, property_1, property_2, .\dots, property_n$, where $n\geq50$, $i\geq 10^6$ We might have missing data in ...
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### Simple method for cluster analysis

One of the academic's at the university where I am studying has conducted research on organisational sustainability. He approached me to turn his research into software that can be used for consulting ...
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### Sorting/Clustering similarity matrices

I wonder, what are the available libraries in R or Python to do correlation matrix clustering (sometimes it is referred to clustering). I also, wonder, after clustering/grouping each point. What is ...
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### Why do we use k-means instead of other algorithms?

I researched about k-means and these are what I got: k-means is one of the simplest algorithm which uses unsupervised learning method to solve known clustering issues. It works really well with large ...
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### How random are the results of the kmeans algorithm?

I have a question regarding the kmeans algorithm. I know kmeans is a randomized algorithm, but how random is it and what results can I expect. Suppose you have clustered a dataset into $4$ clusters, ...
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### How can one show a Kmeans solution is unique?

Suppose we are given a distribution P and a constant K. We wish to minimize the kmeans objective w.r.t centers ${C1,..Ck}$: What constraints on $P$ are known to imply that the optimal solution is ...
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### How do I mathematically prove that k-means clustering converges to minimum squared error?

I am using k-means clustering to analyze and obtain patterns in traffic data. This well-known algorithm performs 2 steps per iteration. Assign each object to a cluster closest to it, based on the ...
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### Kmeans on “symmetric” data

A set is said to be fully-symmetric if for every x in it, negating one of its components results in y such that y is in the set as well. A set is said to be semi-symmetric if for every x in it, ...
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### Data grouping algorithms?

I have numerous one dimensional vectors, $V_1,...,V_i$. Each vector is of variable size composed of natural numbers from different unknown distributions. I'd like to find a way to group/cluster values ...
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### Can sub-optimality of various hierarchical clustering methods be assessed or ranked?

Classic agglomerative hierarchical clustering methods are based on a greedy algorithm. This means that they (many of them) are prone to give sub-optimal solutions instead of the global optimum result, ...
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### Distance independent approximation of Nearest Neighbor/k-NN.

Nearest neighbor/k-NN for use with Normalized Compression Distance. I wonder if there exist any approximation of NN/k-NN algorithm which work for all distance measures ? I would like to test ...
678 views

### Algorithms for clustering documents by similar words and phrases

I'm working on a project where I'm trying to take a pair of documents and find and group (cluster) similar words and phrases between them. Which algorithm would solve this kind of a problem? I know ...
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### How to implement k-means cluster analysis algorithm correctly?

I am trying to implement the K-mean analysis with the Standard algorithm. My implementation seems to work, but I noticed some strange behavior. If the k is close to half of the length of the list to ...
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### Looking for sparse and high-dimensional clustering implementation

I'm looking for a clustering implementation with the following features: Support for high-dimensional data. Now I have approximately 160.000 dimensions/features. Be able to manage sparse matrix. ...
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### Cycling in k-means algorithm

According to wiki the most widely used convergence criterion is "assigment hasn't changed". I was wondering whether cycling can occur if we use such convergence criterion? I'd be pleased if anyone ...
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### Centroid matching problem

For a Dataset $D$, we have gold standard centroids say $c_1, c_2, \cdots, c_n$. Now if we run k-means algorithm on $D$ with input $n$, we get k-means centroid $k_1, k_2, \cdots, k_n$. I just wanted ...
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### Space-efficient clustering

Most clustering algorithms I've seen start with creating a each-to-each distances among all points, which becomes problematic on larger datasets. Is there one that doesn't do it? Or does it in some ...