1
vote
1answer
29 views

Finding optimal combination of parameters for clustering

I have a spreadsheet with one object per line. Each column contains values that are parameters of my objects (let's say length, width, height, weight, color). I can classify objects based on color and ...
0
votes
0answers
26 views

Anomaly detection for one feature vector

I have a $n$-dimensional vector of ordered multiple testing $p$-values and I would like to reject the first values that are under a certain threshold $\alpha$. I am looking at this problem as an ...
1
vote
1answer
75 views

Clustering algorithms for extremely sparse data

I am trying to cluster an extremely sparse text corpus, and I know the number of clusters (my data is the title and author list of scientific publications, for which I already know the number of ...
1
vote
2answers
216 views

K-medoid clustering in python

How do I implement k-medoid clustering algorithms like PAM and CLARA in python 2.7? I am currently using Anaconda, and working with ipython 2.7. I have tried scipy.clusters but they don't seem to ...
0
votes
0answers
25 views

Clustering algorithm for my situation?

Here is my situation. I have a corpus of over 500,000 news. Now I need to cluster the news based on closeness in time and cosine similarity, using vector-space model and TF-IDF weights. I want to ...
1
vote
0answers
26 views

Regression clustering

I am looking for references about classical methods in regression clustering. My problem is the following: I have a cloud of points that are assumed to have been generated by inverse functions with ...
2
votes
1answer
98 views

Name of algorithm (or paper) that scikit-learn cluster.estimate_bandwidth() function implements for meanshift bandwidth selection

Can someone tell me the name of the algorithm (or paper) that sklearn.cluster.estimate_bandwidth implements and is used by the meanshift algorithm implemented in Scikit-Learn to automatically select ...
2
votes
0answers
48 views

Expectation Maximization Clarification

I found very helpful tutorial regarding EM algorithm. The example and the picture from the tutorial is simply brilliant. Related question about calculating probabilities how does expectation ...
1
vote
3answers
393 views

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 ...
0
votes
2answers
109 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 ...
1
vote
1answer
116 views

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 ...
5
votes
1answer
2k views

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 ...
0
votes
0answers
54 views

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 ...
0
votes
2answers
104 views

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 ...
2
votes
0answers
405 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 ...
2
votes
2answers
1k 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
253 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, ...
5
votes
2answers
209 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
812 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
247 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, ...
5
votes
2answers
257 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
91 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
915 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
483 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
416 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
324 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
259 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 ...
9
votes
3answers
197 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
155 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
2k 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 ...