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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33 views

Testing for cluster structure in one dimension

I have a set of points along an interval. What is the best significance test to measure clustering of the points in the interval (deviation from a uniform distribution)? I've added two examples below ...
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0answers
26 views

Checking the assumptions of K-means clustering

I want to do a k-means clustering on a dataset containing 22 numerical variables between 0 and 100 and 75 observations using R. I read this post How to understand the drawbacks of K-means on k-means ...
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3answers
35 views

Checking quality of clustering of labeled-class data

I'm performing clustering on a labeled dataset. I would like to check the quality of clustering. Is there a well accepted way of doing that? So basically I would like perform some classification-like ...
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2answers
80 views

What is the best algorithm to find similar text documents?

I have many text documents and I would like to find similar documents to each document within my data set. Is Latent Dirichlet Allocation (LDA) the best way to do that, or are there other algorithms ...
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0answers
17 views

Comparison of distributions

I measured the velocity of particles depending on some biophysical conditions (summarized data from two DoE plans) for about 100 samples. The goal was to identify the most important parameters ...
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1answer
138 views

How to generate new Topic for new documents?

what approach would help me generate new topics for new documents? I read this page in order to learn more about the effect of specifying keywords for the topics that we care about detecting in new ...
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2answers
21 views

What are some good (and fast) alternatives to dynamic time warping?

I am planning to cluster tens of thousands of time series of different lengths into two groups.
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12 views

Clustering Standardized Mortality Ratio

I am bit new to the whole clustering idea. I have a data set that gives information about the SMR in the different states of America from 1995-2000. Hence I want to apply clustering techniques ...
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1answer
242 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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1answer
93 views

Hierarchical Dirichlet Processes in topic modeling

I think I understand the main ideas of hierarchical dirichlet processes, but I don't understand the specifics of its application in topic modeling. Basically, the idea is that we have the following ...
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1answer
36 views

Cluster analysis in bounded data

I have 261 vectors with 9 attributes. Each attributes contains numbers between 0 and 1. I am not sure what the most appropriate clustering method for this kind of data is. Initially, I used the ...
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1answer
596 views

Clustering text with python

I have asked on StackOverflow, but they suggested me to move here for better answers. I copy paste the question. I have decided to play a little with similarities and clustering text. I have already ...
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1answer
19 views

Change in r squared due to clustering in multiple linear regression

Puny undergraduate stats student here. I am examining the effect of two regressors on a predictor. OLS on the raw data (approx 200k cases) yields next to no correlation in the following models: ...
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2answers
72 views

Mixing probabilities in mixture models using EM

Assume we want to estimate the mixing probabilities ($\pi_{k}$) for each member distribution in the mixture model. We know that $\sum_{m}^{K}\pi_{m}=1$, so we can formulate the optimization problem ...
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0answers
25 views

Power calculations for proportions, two-stage cluster

I am trying to do power calcs for a survey. It's not an RCT, so I have encountered a dearth of material on this. We are trying to estimate a proportion within a population, and have two stages of ...
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1answer
251 views

Quantitative results of clustering analysis

Currently, I am doing a clustering analysis for two sets of data. One smaller dataset (about 100 data) got ground truth labels, and one larger dataset (about 2000 data) has no ground truth labels. ...
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1answer
36 views

What clustering algorithm should I use for clusters spaced on a grid?

I have some data sets of clusters of points arranged more or less on a regular grid. The data sets have these properties: The data is two, three, or maybe rarely four-dimensional. I know in advance ...
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0answers
8 views

cross validation for non parametric clustering methods: dimensionality reduction possible?

I do have about 100 data points gathered during a DoE experiment. The response variable was the settling velocity distribution depending on 10 factors. I analysed the 10 % percentile of the ...
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1answer
23 views

What is the relationship between differential analysis and hierarchial clustering?

I'm currently in an internship for R bioinformatics, where I'm writing software for single-cell RNA sequencing analysis. We're looking for differentially expressed genes between groups, but I don't ...
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0answers
18 views

Spatial clustering based on response

Statistics version: I have a few measurements of a function that takes three inputs and produces a few 2D fields of outputs: f(a,b,c;x,y), with f being a vector of several quantities. I would like to ...
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0answers
10 views

Robust Sparse K Means clusters and valid index

I used the robust sparse k-means for clustering my dataset and I would like to calculate some distance-based statistics for evaluating my results. Should I compute them on the dissimilarity matrix ...
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0answers
5 views

Algorithms for association rule mining (or alternatives) to “cluster” continuous outputs in supervised settings

I am collaborating with experimentalists who obtained measurements on a continuous scale 0.0 - 1.0, and each sample has ~30 binary features. They basically want to "learn" from this data, for example, ...
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1answer
26 views

Testing Clustering Variables

I have two clusters. Those two clusters were obtained using Fuzzy C-Means with 8 variables. I'd like to know which variables have important role in differentiating the two clusters. Can I use t test ...
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1answer
18 views

Functional clustering with R [closed]

I have a time series data in R, and I am using functional clustering. I would like to interpret a figure that is output below the code. Furthermore, I would like to control line colors and thickness ...
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1answer
148 views

Agreement of clustered data

I have the following situation: I have analyzed several data curves from a group of patients (16 curves per patient) with different analysis methods and want to test for the agreement of the methods. ...
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2answers
37 views

K-means - comparing solutions with SSwithin elbow-method: minimum “too early”

I am running a k-means clustering process in R and I'm comparing cluster partitions of different number of clusters: k = from 1 to 17. Using the elbow-method, I have a minimum at k=5, but this value ...
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0answers
57 views

Data Preparation for Cluster Analysis

Updated answer to "Data Preparation for Cluster Analysis": Based on the discussions, data normalization and removing correlation among data are often recommended. References posts: 1) Are mean ...
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2answers
84 views

How to find similar documents in a big data set

I have many text text documents and my goal is to find similar documents. Apparently it is a clustering type of question and LDA (Latent Dirichlet Allocation) is a good candidate to do that. However ...
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2answers
5k views

Kmeans: Whether to standardise? Can you use categorical variables? Is Cluster 3.0 suitable?

I am running kmeans for a market research study, and I have a couple of questions: Should I be standardizing my data, and if so, how? For example, one variable I have is product demand, which is ...
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2answers
46 views

Interpreting kmeans output

I am working on a clustering model with the kmeans() function in the package stats and I have a question about the output. My data is a sample from several tech companies and AAPL._UP is a variable ...
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0answers
29 views

Clustering using gower distance in R [migrated]

I have a dataframe which has categorical and numeric variables. I want to cluster this data using gower distance and get cluster values as a vector as in kmeans function. How can i achieve that?
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0answers
26 views

How to deal with empty values in a cluster analysis

I'm currently working on my master's thesis. Part of the work is a customer segmentation by means of a cluster analysis. One variable for the cluster determination should be the chronological ...
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0answers
28 views

Average linkage clustering

I have a matrix with proximity values $$ \begin{matrix} &1&2&3&4&5\\ 1& 1 & 0.9 & 0.1 & 0.65 & 0.2\\ 2& & 1 & 0.7 & 0.6 ...
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1answer
977 views

SSB - Sum of squares between clusters

I got a little confused with the squares and the sums. As far as I know, the variance or total sum of squares (TSS) is smth like $\sum_{i}^{n} (x_i - \bar x)^2$ and the sum of squares within (SSW) ...
3
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1answer
348 views

Time Series Data Mining Library?

Can anyone recommend a library for time series data mining tasks other than predictive modeling and statistical analysis? There seem to be a number for these purposes (e.g., Gretl), but nothing for ...
4
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1answer
274 views

Partitioning Around Medoids (PAM) with Gower distance matrix

My data is is mostly continuous but has one binary variable. I tried the pam algorithm in R with the Gower index, but the number of clusters that give the best ...
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1answer
16 views

If two domains measure the same thing how to approach a cluster analysis?

I would like to perform a cluster analysis on my sample with a set of variables categorized in several domains. This is just fine but my problem is that two domains (one consisting of four and one of ...
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2answers
505 views

Clustering of points based on vector feature similarities in R

I have as an input a number of points that I need to partition into clusters. Each point has a number of features that are ideally to be used to find the similarity between each point and the others. ...
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3answers
114 views

Analysing data on importance ratings

I had following question in my questionnaire: Rate the following factors: price, quality, advertisement, brand, reference from 1 (very important) to 5 (least important) that may have influenced your ...
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1answer
39 views

Interpret the visualization of k-mean clusters

Following my posted data here, I conducted a k-mean clustering analysis. I refereed to this post: How to produce a pretty plot of the results of k-means cluster analysis? for the clusters ...
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1answer
31 views

A non parametric clustering algorithm suitable for high dimensional data

What are suitable clustering algorithms for high dimensional data, where I do not have to input a predetermined number of clusters?
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1answer
16 views

Clustering sequence on similarity using percentage identity matrix

I have a set of 400 nucleotide sequences and want to cluster them on basis of similarity. For clustering, I am expecting a similarity <=45% among members of a cluster. Also, there will be a few ...
2
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2answers
135 views

Relevance of overall absolute values in covariance analysis of two variables

I am performing K means clustering on a gene expression dataset. I am aware of the fact that the Pearson correlation metric allows to group trends or patterns irrespective of their overall level of ...
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1answer
666 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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4answers
316 views

How I can convert distance (Euclidean) to similarity score

I am using $k$ means clustering to cluster speaker voices. When I compare an utterance with clustered speaker data I get (Euclidean distance-based) average distortion. This distance can be in range of ...
2
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2answers
56 views

K-means: Why minimizing WCSS is maximizing Distance between clusters?

From a conceptual and algorithmic standpoint, I understand how K-means works. However, from a mathematical standpoint, I don't understand why minimizing the WCSS (within-cluster sums of squares) will ...
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4answers
9k views

Why does k -means clustering algorithm use only Euclidean distance metric?

Is there a specific purpose in terms of efficiency or functionality why the k-means algorithm does not use cosine similarity as a distance metric, but can only use the Euclidean norm?
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4answers
7k views

Clustering a dataset with both discrete and continuous variables

I have a dataset X which has 10 dimensions, 4 of which are discrete values. In fact, those 4 discrete variables are ordinal, i.e. a higher value implies a higher/better semantic. 2 of these discrete ...
4
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1answer
324 views

Clustering data that has mixture of continuous and categorical variables

I have data that represent some aspect of human behavior. I want to cluster it (unsupervised) into behavioral profiles of some sort. now, some of my variables are categorical (with 2 or more ...
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2answers
46 views

Dig deeper on “Determine the Number of Clusters and Validate It”

Updates to this thread: Based on Anony-Mousse's comments on my current results, there is only one big cluster in my data set. However, I think it might still be possible to reveal the clusters if I ...