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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29 views

Converting a similarity to a dissimilarity [duplicate]

I am working on a clustering problem for which I have to manually choose the number of clusters. I have a visualization tool that helps me decide whether the clusters are good. In order to ...
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26 views

What is a shift bicluster?

I am reading A comparative analysis of biclustering algorithms for gene expression data (Eren, Kemal, et al. - 2013) When explaining the Cheng and Church method, it says that: MSR was shown to be ...
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22 views

updating clusters in NCut spectral clustering

Are there any methods to update the clusters formed in k-way NCut when new data points arrive or any change in the similarity matrix?? thanks
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13 views

Clustering with dependent features

I have $n$ observations which I want to cluster based on $p$ features. Those $p$ features are dependent to each other (or correlated). If I use Euclidean distance, the weights of all features would be ...
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42 views

Vector Quantization of heavy tailed distribution

I'm generating with Monte Carlo simulation some stock price $X$. Once I have the stock price sample, I want to cluster it with 100 points $\hat{X}$. My problem is that the error associate with my ...
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1answer
37 views

Data Conversion to Standard data format in hierarchical Dirichlet process

I'm trying to test the performance of posterior inference on a set of documents with hierarchical Dirichlet process for topic modeling. How can i convert my data (document) to standard data format ...
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1answer
50 views

Can component scores be used for further analyses, e.g. cluster analysis?

I have done a principal component analysis using SPSS and now have 3 components. 2 components have 4 items in the subscale, and 1 component has 3 items. Component scores using regression for each ...
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35 views

Time series classification & hierarchal clustering

I have a need to build a classifier for time series data. I've made progress but would like advise on my current challenge (see below). My research led me to KNN and Dynamic Time Warping as the ...
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45 views

Advice on how to analyse “customer-data” in R

consider the following example data: ...
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1answer
59 views

Finding the cluster centers in kernel k-means clustering

I think this is the most easily understood topic in Kernel K Means Clustering. But assuming that I am not an expert in Machine Learning, can someone tell me how does someone calculate Kernel K means ...
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61 views

How to form Clusters from a Similarity Matrix in Matlab

I have a matrix stored in the matlab workspace that has similarity measures between documents stored using one of the similarity measure algorithm. Now, I want to identify the elements which have ...
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40 views

State-of-the-art in deduplication

What are the state-of-the-art methods in record deduplication? Deduplication is also sometimes called: record linkage, entity resolution, identity resolution, merge/purge. I know for example about ...
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1answer
24 views

Converting FuzzykMeans to SphericalFuzzyKMeans?

I grabbed an implementation of FuzzyKMeans (FuzzyCMeans) from the nightly build of the Apache Commons Math library, but I now realize I need to use Cosine Similarity instead of the Euclidean Distance. ...
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1answer
65 views

Determining similar users from hierarchical clustering

I use hierarchical clustering to cluster users which are similar to each other based on a Jaccard coefficient. I have now coded a solution to extract similar users based on hierarchical clustering: ...
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3answers
151 views

Can we use clustering output as predictor variable for classification?

Can we use clustering output as predictor variable for classification? I have a set of data and I do clustering analysis on it, it divides the data into different clusters. Can I use this cluster ...
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0answers
23 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 ...
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0answers
11 views

scale for cluster quality measures range

Is there any standard scale exists for cluster quality measures? Means after measuring cluster quality using different inter-cluster metrics( LCQ, L-Separability, Variance Ratio, Additive Margin), I ...
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0answers
29 views

How to calculate the similarity of two corpora (each of which contains a set of documents)?

I have two corpora for example, each of which contains a set of different documents, and each document are already represented as a vector of words in a certain way. The two corpora are small, only ...
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39 views

How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
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39 views

Exact derivation for finding k-means from Gaussian Mixtures

I am having difficulty in deriving k-means from Mixture of Gaussians. I am following the notation from Bishop (2006), Section 9.3.2: Suppose we have : $$ p(\mathbf{x}| \boldsymbol{\mu}_k, ...
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1answer
49 views

What do you do when a centroid doesn't attract any points?

I am solving a clustering problem on a trivial dataset with the k-means algorithm. I am running a couple of tests and, from time to time, one centroid doesn't attract any points, i.e. I've got an ...
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1answer
50 views

Clustering algorithms assigning probability values

I have a distance matrix for some data I want to cluster. However, I don't just want to assign elements to clusters, but I also want to assign a probability for each element to belong to each cluster. ...
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39 views

Maximam r distance for Ripley's K-function

I am using R's package spatstat to study the locational pattern of conflict events in Africa (around 8.000 points) using point pattern analysis techniques. I was able to obtain the plot of g(r), the ...
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3answers
185 views

Is it important to scale data before clustering?

I found this tutorial, which suggests that you should run the scale function on features before clustering (I believe that it converts data to z-scores). I'm wondering whether that is necessary. I'm ...
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25 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 ...
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0answers
20 views

How to find probability distribution of a multi-attribute datapoint in a dataset

I have a dataset with certain number of multi-attribute tuples. Each of the attribute values is a continuous random variable. I want to model each tuple (rather each attribute of the tuple) by a ...
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1answer
44 views

got stuck in Cluster analysis, way forward?

I have a situation, where I need to classify items into groups (lets say 6). When I ran k-means 90% of my data fall in 1 group remaining 10% fall in other groups. What's next step? In order to further ...
2
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1answer
47 views

Multivariate data analyis of compositional data

Suppose I have a multivariate, compositional dataset that depicts the concentration of different elements. However, the data are not available on a single scale; i.e., some are of form 0.00x while ...
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2answers
89 views

What do you do when there's no elbow point for kmeans clustering

I've learned that when choosing a number of clusters, you should look for an elbow point for different values of K. I've plotted the values of withinss for values of k from 1 to 10, but I'm not seeing ...
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1answer
71 views

K-medians, formula to compute the median

If you are running K-medians, and your distance metric is the L1 norm, how do you derive that the center of each centroid is the median of the data points assigned to it? Second, how do you compute ...
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29 views

Can one-dimensional cluster analysis factor in variance increasing with covariate?

I have several data sets of frequency values (See Fig. 1 for an example). I'm interested in those tighter clusters (marked by green rectangles) and am using hierarchical clustering in MATLAB (with ...
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1answer
28 views

string clustering: similarity criterion

I have a set of strings of dimension $10,000$. I want to group similar strings together in one group, perform clustering. As string metric, I am using the ...
4
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1answer
77 views

What algorithm should I use to cluster a huge binary dataset into few categories?

I have a large (650K rows * 62 columns) matrix of binary data (0-1 entries only). The matrix is mostly sparse: about 8% is filled. I would like to cluster it into 5 groups - say named from 1 to 5. I ...
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67 views

Clustering email with mixed types of attribute

I am looking to cluster thousands of emails in one's mailbox. Different from traditional analysis with emphasis on email body, the attachments will play a big role in my work. The data set contains ...
2
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1answer
138 views

Run time analysis of the clustering algorithm (k-means)

I was reading some notes on ML and clustering and it claimed that the run time of clustering was O(kn) where k is the number of clusters and n is the number of points. I was wondering why this was ...
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2answers
30 views

How to cluster users based on search terms

How to cluster based on what users are searching on I'm working on an app which includes search functionality: a search box that allows a user to enter text and search the entire site. I have access ...
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1answer
85 views

String clustering and centroid computation

I have a text file document containing a set of words strings that I want to cluster. I want to use the K-means algorithm. As a ...
2
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1answer
79 views

Evaluating the clustering of a Kohonen UMatrix

Given a converged Kohonen feature map, how would one evaluate the clustering in terms of intra- and inter-cluster distances? Assuming that both the trained codebook vectors and Unified Distance ...
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1answer
322 views

Rand index calculation

I'm trying to figure out how to calculate the Rand Index of a cluster algorithm, but I'm stuck at the point how to calculate the true and false negatives. At the moment I'm using the example from the ...
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0answers
37 views

Tuning the inflation parameter in mcl

I read on the mcl documentation that the inflation parameter can be used to tune the granularity of the clusters. I am not very familiar with graph theory. What is the granularity of the clusters? Can ...
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22 views

linear regression after cluster analysis (How much information did I lose)

I had dataset of the type $y;x_1, x_2, x_3, ... $ and I used some of my variables to perform a cluster analysis. Let's say one of those variables that I used was $x_i$ Then. After I had my clusters. ...
2
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1answer
67 views

Ascertaining what clustering algorithm to use in various situations

It is said that kMeans clustering works as long as we don't have clusters of differing sizes, densities, and non-spherical shapes I understand how one might check the sizes and densities of data ...
2
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1answer
220 views

A routine to choose eps and minPts for DBSCAN

DBSCAN is most cited clustering algorithm according to some literature and it can find arbitrary shape clusters based on density. It has two parameters eps (as neighborhood radius) and minPts (as ...
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65 views

Relative Variable Importance in Clustering

In SPSS, the user can check the relative variable importance in a clustering result and produce a graph like the following: link: ...
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2answers
113 views

What's the easiest way to separate two populations in a scatterplot?

I have to separate two populations by a line in a scatterplot: I would like find a threshold that separates the two populations. In @Waynes words, I would like to cluster the points into two ...
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3answers
69 views

Converting a distance to a similarity

I am working on a graph clustering algorithm (mcl). It gives the opportunity to give weights to the edges. The weights must be similarities, but I have a distance. The values of this distance range ...
2
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0answers
34 views

Choice of an evaluation metric for a graph clustering algorithm

I have instances for which the only thing I know is 70% of the distance matrix. I know some of these points form groups of correlated points (each point of a group is "close" to every point of the ...
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54 views

Cluster evaluation based on different similarity algorithms

At the moment I'm researching clustering of single words. The input of this research is a list of words (unigrams). During the research I want to compare several similarity algorithms to see how they ...
4
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1answer
54 views

gaussian mixture model - approximate a matrix

I have a similarity matrix M - the value M(i,j) indicates the similarity between two elements i and j. I want to approximate that matrix using a Gaussian Mixture model or I want to cluster that ...
3
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2answers
168 views

Using the gap statistic to compare algorithms

I want to compare the performances of two clustering algorithms that give me different numbers of clusters. I recently learned about the gap statistic. However, from what I have learned, this ...