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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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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0answers
38 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
177 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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0answers
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 ...
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1answer
43 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
86 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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0answers
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
27 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
75 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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0answers
65 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
135 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
80 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
78 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 ...
1
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1answer
318 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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0answers
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
205 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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64 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
68 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 ...
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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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0answers
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 ...
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1answer
51 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 ...
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2answers
165 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 ...
3
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1answer
57 views

Finding clusters to fit least squares and produce a piecewise equation

In the figure below, I've manually drawn an approximate solution to a least squares fit of the associated regions separated by black lines. The data appears to be bounded by two asymptotes (y=-18 and ...
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2answers
129 views

Is clustering (kmeans) appropriate for partitioning a one-dimensional array?

I want to group the outcome of a function into 2 (or 3) categories. I have a function efficiency=f(weight,speed,#refueling_stops) that takes 3 input parameters and the output tells me how "efficient" ...
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1answer
46 views

Calculating (dis)similarity between different types of features

Disclaimer: I understand that this question is specific to the types of data, the end goal, etc. but I just wanted to get some quick tips regarding calculating dissimilarity between different types of ...
3
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2answers
305 views

Cluster analysis on panel data

I have a panel data set (country and year) on which I would like to run a cluster analysis by country. My data set has around 20 variables. Here's a summary for my panel data: ...
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1answer
72 views

Leaf ordering for hierarchical clustering dendrogram

Assuming merging process was completed and we have the history of n-1 merged clusters (merge two clusters p and ...
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1answer
51 views

Modeling techniques for dichotomous data

I have dichotomous data where some of my independent variables are categorical, some are continuous and some are binary (0/1). My dependent is a binary response (Fail/NoFail, 0/1). The data is some ...
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1answer
55 views

How to define silhouette for one cluster?

I want to compare two clustering algorithms. I took data that the first algorithm gathered in one cluster. The second algorithm gave 3 clusters for the same points. In order to compare the results, I ...
3
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2answers
107 views

Categorical variable with a very large number of categories as a predictor

I am trying to use a categorical variable as a predictor in a supervised learning setting, but there are too many categories for the classification algorithm to handle, something like over a 1000 ...
0
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3answers
171 views

Scalability of Markov Clustering

I want to do graph clustering on a large dataset (A graph with 600,000 Nodes and tens of millions of edges). I read about Markov clustering. I saw this algorithm involved the calculation of a ...
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1answer
54 views

k-medoids algorithm with incomplete distance matrix

I want to apply k-medoids algorithm using an incomplete distance matrix as input. How can I handle the lack of information of this matrix? Just ignoring the missing distances? Or is there a better ...
1
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1answer
47 views

k-core clustering algorithm

I am trying to cluster data. Each point in this dataset is connected to some other points. I want to define clusters "depending on how much the points are connected to each other". After some ...
0
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1answer
62 views

distortion function for k-means algorithm

I was reading Andrew Ng's ML lecture notes on K-mean clustering, in which the distortion function is defined as follow $$J(c,\mu) = \sum^m_{i=1} || x^{(i)} - \mu_{c^{(i)}}||^2$$ I am puzzled about ...
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21 views

contingency table and noise

I have a contingency table and I want to use it to evaluate my clustering method's results (e.g. RandIndex). I have also noises in my data which are labeled as NOISE and they obviously should not be ...
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0answers
46 views

Clustering time-shifted sales time-series

I need to perform clustering and classification of time series of weekly sales of different products. My data are weekly sales of different products in differest areas (stores). The challenges on this ...
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0answers
37 views

Random initialization with k-means clustering

I read on my machine learning course (on coursera) that random initialization performed several times and then taking the cluster with the lowest cose could help when the number of clusters is ...
0
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0answers
40 views

using outcome of clustering as an ordinal scale for regression - feasible?

Suppose I have access to five dental surgeries, who volunteered to collect data about patients and their regular check-ups. The dental surgeries are quite up to completely different from each other ...
0
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1answer
261 views

Multidimensional scaling using Python

I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a ...
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0answers
55 views

One pass distributed clustering algorithm

I am working on clustering of data streams. For my purpose Sequential Leader Clustering (SLC) have given fair result, as i need not give number of clusters (like k in k means), which require some ...
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1answer
250 views

Selecting an appropriate machine learning algorithm?

I do not think that this is a difficult question, but I guess someone needs experience to answer it. It is a question that is asked a lot here, but I did not found any answer that explains the reasons ...
4
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3answers
217 views

Can you compare different clustering methods on a dataset with no ground truth by cross-validation?

Currently, I am trying to analyze a text document dataset that has no ground truth. I was told that you can use k-fold cross validation to compare different clustering methods. However, the examples I ...