Tagged Questions

0
votes
0answers
18 views

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 ...
1
vote
2answers
110 views

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 ...
4
votes
2answers
62 views

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, ...
1
vote
1answer
102 views

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 ...
3
votes
3answers
213 views

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 ...
2
votes
1answer
214 views

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, ...
0
votes
0answers
40 views

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 ...
4
votes
2answers
177 views

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, ...
1
vote
1answer
64 views

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 ...
5
votes
2answers
510 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 ...
1
vote
1answer
381 views

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 ...
4
votes
3answers
278 views

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. ...
6
votes
3answers
217 views

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 ...
6
votes
3answers
212 views

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 ...
8
votes
3answers
149 views

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 ...
2
votes
1answer
137 views

A smart way of clustering a collection of sets based on an inherent hierarchy

Given a collections of sets, which have an inherent but unknown (at runtime) hierarchy, I would like to cluster them based on the sub/super-relationships with respect to their elements. Let me try and ...
6
votes
3answers
1k views

How do you test an implementation of k-means?

Disclaimer: I posted this question on Stackoverflow, but I thought maybe this is better suited for this platform. How do you test your own k-means implementation for multidimensional data sets? I ...