Cluster analysis is the task of 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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2 Different F1-Measure to calculate clustering performance - which one is correct and why?

I know it sounds incorrect but that is the truth Here let me show you This below one is the first one and very widely used in the literature First one reference : Steinbach, Michael, George ...
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17 views

Does Newman clustering work on weighted graph with non-integer weights?

I have a weighted undirected graph, where weight is distance and it is between 0 and 1. I want to apply the weighted version of Newman clustering. I think weight must refer to strength or similarity, ...
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10 views

How to predict new data goes which cluser in R [duplicate]

I already have k means output and i have segmented my users accordingly. Now, I have to predict cluster number for new users whenever they come. Do I have to run kmeans each time a new user comes into ...
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1answer
24 views

Fourier transform clustering

Which clustering algorithm would you use, moreover which distance measure, in case of analysis in frequency domain? I would like to perform Discrete Fourier Transform on time series and perform ...
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1answer
18 views

How to build a distance function given a cluster of points?

Given a non-elliptical cluster of points in a n-dimensional space I would like to get a distance function from the centroid of this cluster such that its "equipotential" surfaces has the same shape as ...
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2answers
42 views

Clustering methods ⊂ Unsupervised learning

Is it proper to say that clustering methods are mostly unsupervised learning techniques, with some exceptions such as model-based clustering?
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66 views

Stata: How to plot groups of variables side-by-side in stacked percent bar chart with subgraphs? [closed]

I did a cluster analysis of categorical variables and want to plot the result in a summary graph. There are three groups of variables that contain 'dummy variables'. I'm able to plot one group of ...
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1answer
21 views

Data to use for cluster analysis

I have a data frame of employees hours at work. The variables are time coming to work, time going home (finishing work), and time worked for the day, which is not the difference of going home and ...
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19 views

Rearrange 2D grid [closed]

I have a 2D grid on which I represent data points: Here, the red data point activates the grid on positions (1,1), (1,4) and (2,3). The blue data point activates the grid on positions (1,1), (4,3), ...
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15 views

Categorical Clustering of Users Reading Habits

I have a data set with a set of users and a history of documents they have read, all the documents have metadata attributes (think topic, country, author) associated with them. I want to cluster the ...
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26 views

Clustering Spatial Data while Maximizing a Constraint

I'm trying to perform a spatial clustering assignment by minimizing spatial distance while maximizing total weight within each cluster. My Data My data contains 3 columns and approximately 170 rows ...
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1answer
34 views

Cluster Boostrap with Unequally Sized Clusters

I need to perform a bootstrap for variance estimation on a GEE model for clustered data that I am analyzing. I understand that I need to use a clustered bootstrap for this, which is pretty much the ...
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1answer
35 views

How to combine the results of several clustering with scikit-learn?

I am trying to fit several cluster algorithms on one or across several subsets of a data matrix X, of shape (n_samples, n_features). For example: ...
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1answer
26 views

What is the relation between linkage and hierarchical clustering

I am self studying hierarchical clustering and got confused about the concept of linkage, could anyone explain what does it mean? what role does it play in what type of clustering? Any input will be ...
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1answer
21 views

In what way does clustering help in classification?

I understand that with a K-means or DTW algorithm one can cluster time series using a distance criterion, i also understand that with a K-NN algorithm for example one can do pattern recognition and ...
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24 views

Joint probability distribution function between different time-series clusters

I have 24-hour time-series data-sets for Solar Power and Power Consumption respectively for an entire year i.e 365x24 data set. Intuitively, the data set captures the variation of each of the ...
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1answer
44 views

proper Similarity measure and clustering algorithm for binary data

data sample as follow : interest to find clusters of similar users, pages number around 100 pages. users around 1000 , i would like to know what are proper Similarity/dis. measure can used in this ...
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1answer
16 views

Algorithm that partitions a set coordinates around X number of fixed centroid coordinates?

I understand that in K-means you select how many clusters you want and the result is the location of each centroid. How does one handle a situation when you know how many and where each centroid is, ...
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1answer
49 views

Useful type of clustering method

I have a set of points in $R^3$ whose volume is increasing as time goes by. They tend to be clustered but I don't know how many. Also, the number of clustering might be changing when new points enter. ...
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43 views

Why is it that a larger 'k' value fails to converge but a smaller 'k' converges?

I'm doing clustering via GMM, which is initialized first by k-means. I am using a data matrix that cannot be classified as small by any standards, they are usually of the size ...
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25 views

merge small clusters in R [closed]

In R, I have cut a dendrogram into clusters. However some of the clusters have only few samples. How can I merge the small clusters with nearest big cuter. ...
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2answers
43 views

Cluster real numbers

I have a set of precise measurements, and what I want to do is count the frequency (how many time it appears) for each value. The problem is that these are very precise measurements and with a naive ...
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1answer
22 views

What quality measures can be used to evaluate a density-based clustering algorithm?

I have a weighted undirected graph, where weight is the similarity and range from 0 to 1. I applied a density-based clustering method and get some clusters, with overlapping nodes (node can belong to ...
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19 views

Need Help in Sampling

Can anyone help me understand the sampling method that needs to applied for this study? I am struggling with Straified Multistage Sampling and Stratified Cluster Sampling. The study is on foreign ...
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14 views

Similarity search in chromatographic data sets

Introduction I am doing research on ranking chromatographic data with respect to their similarity. I have several difficulties in getting started with a formulation of my problem. The data is given ...
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1answer
41 views

Clustering to minimize variance and maximize frequency in the cluster

Doesn't matter what clustering algorithm it is. Lets say I have data in a 2 Dimension space. (X Y). I want to tune (select parameters in) my clustering algorithm so that I minimize the variance in ...
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35 views

Topology of Confidence Intervals

I hope this is the right site to post this. The example I have in my mind is a GLMM model, where we infer random effects, and a random effect caterpillar plot (with confidence intervals): Now, ...
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2answers
66 views

Use a combination of grand mean and group mean centering to standardize variables

I'm using cluster analysis to examine profiles of three variables, X1, X2, and X3. Because ...
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2answers
39 views

Clustering methods that take data order into account

Is there any clustering methods that allows to take the time information (i.e. data order) into account ? That is, in addition to maximising intra-cluster similarity and minimising inter-cluster ...
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11 views

Dirichlet group assignment

As in the Dirichlet clustering, the dirichlet process can be represented by the following: Chinese Restaurant Process Stick Breaking Process Poly Urn Model For instance, if we consider ...
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3answers
112 views

Suspicious results after clustering

I've done a clustering and I think that my results are too good to be trusted. Here is my pipeline: Inputs: a dataset of 208 images, distributed into 2 classes (99 and 109 images in each class). ...
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1answer
71 views

Clustering crime data which has {latitute, longitude, crime-type} tuples

I have a data set which has thousands of rows of {latitute, longitude, crime-type} tuples. Sample Data: ...
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43 views

Can SVD be used to perform factor analyis?

What is the relationship between SVD and factor analysis? How can use singular values and other matrices from SVD to perform factor analysis or cluster document-term matrix without using other ...
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1answer
23 views

Is there a method to map clusters created for two independent data sets with certain common parameters?

Was thinking of a problem, but not yet clear on the exact statement.Please excuse the vagueness. The general idea I have might be explained using the following example: The two sets of data are ...
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1answer
22 views

Clustering based on SVD

I have a document-term matrix and I performed SVD on it. How can I cluster terms based on the singular values? Is there any relationship between SVD and factor analysis?
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1answer
54 views

I want to show a local optimum in my paper, how do I generate the data for it?

I'm writing a paper where I am explaining the problems of local optimum in my clustering algorithm. While clustering, in my data I would at times get local optimums. But I've tried and I cannot ...
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37 views

A high cophenetic correlation coefficient but dendrogram seems bad

I have 2 results for the same dataset. One is hierarchical clustering using Ward's method and I got 0.75 cophenetic correlation coefficient. The second is average method and I got 0.91 cophenetic ...
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1answer
30 views

What kind of data preprocessing is required before running a clustering algorithm?

I have a dataset that consists of 87 observations or rows of data. My variables are a mix of different kinds - continuous, categorical and some count. Examples are variables which are percentage ...
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1answer
51 views

R: What is the gap statistic reference distribution in the clusGap function in the library(cluster)?

I am doing a project on the Gap Statistic from Tibshirani etc http://www.web.stanford.edu/%7Ehastie/Papers/gap.pdf On page 4 of the pdf in section 4 on "The computational implementation of the gap ...
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26 views

Elbow method implementation for hierarchical clustering

I've got a dataset that I need to divide intro clusters using hierarchical clustering algorithm. I've decided to try to employ an Elbow Method as a way of determining optimal no. of clusters k. ...
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1answer
107 views

User segmentation by clustering with sparse data

Imagine that I have 100k users and 1k categories. For each user, up to 5 categories, I know how much money they have spent. Obviously my data is very sparse. Now I want to group users by the money ...
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1answer
36 views

Clustering a database of strings based on their similarity to a seperate set of words

I have a list of strings that I have extracted from a large database of strings. These "blacklisted" strings have been removed but I also want nothing similar to them present in the database as well. ...
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22 views

Special case of clustering in one dimension

Given an array of positions in an X-axis and each position is associated with a Group. An Example is given below: ...
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47 views

Why is the k-means++ algorithm probabilistic?

The k-means++ algorithm provides a technique to choose the initial k seeds for the k-means algorithm. It does this by sampling the next point according to a multinomial distribution over the unchosen ...
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1answer
57 views

Interpretation of the cluster criterion $\operatorname{tr}(S_W^{-1}S_B)$

There is a cluster criterion defined as: $$\mathcal{C} = \operatorname{tr}(S_W^{-1}S_B) = \sum_{i=1}^d \lambda_i,$$ where $\operatorname{tr}$ is the trace, $S_W$ is the pooled within-group scatter ...
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25 views

Clustering subjects regarding binary property vectors

My data set consists of 120 subjects and 50 binary attributes. I want to find clusters in that. So far I started by a visual analysis plotting the n x n matrix taking the several similarity measures ...
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8 views

choose singular vectors in co-clustering

I have a m*n Term-Document matrix A,Bipartite Graph matrix $$ G= \begin{matrix} 0&A\\ A^T&0\\ \end{matrix} $$ DegreeMatrix $$ D= \begin{matrix} D1&0\\ 0&D2\\ \end{matrix} $$ in the ...
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16 views

Derivation of the third moment of the count joint statistic

Does anyone know where I can find the derivation of the third moment of the joint count statistic? I found this similar question answered in the past: need derivation of join-count variance (spatial ...
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1answer
67 views

How can I cluster products based on market basket data?

I need to cluster products based on market basket data, i.e. I have a data table with sold products and the respective orders and I want to cluster products so that products within a group are bought ...
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1answer
67 views

K-means algorithm's EM “Maximization” step

I'm a software engineer and am trying to understand how Lloyd's K-Means algorithm fits into the general framework of the Expectation-Maximization (EM) algorithm. I previously read the question ...