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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4 views

Extract distance matrix for mixed variables (categorical and numerical) from the following code

I have a data that consider mixed data type ( Numeric and categorical [binary, ordinal, nominal]). I want to apply partition clustering on my data. I found the following paper A k-mean clustering ...
-1
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0answers
6 views

When to stop agglomerative hierarchical clustering - stopping criteria [on hold]

I am coding my application each function so i am not using tools which does everything for you Been looking for solution when to cut my agglomerative hierarchical clustering How do i cluster? I ...
1
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1answer
19 views

Is there anything wrong with performing EM clustering on PCA output?

I am trying to separate my dataset into meaningful clusters. I have tried k-means, hierarchical and EM clustering (fitting a gaussian mixture model using EM algorithm, using the EMCluster R package) ...
2
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1answer
156 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. ...
2
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0answers
8 views

Power calculation for multiple level clustering / randomization using simulations R

I am trying to calculate power of the following design: -Treatment is randomized over a small number of clusters (1st level clustering=regions) -Within each region we randomly select villages ...
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2answers
151 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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1answer
686 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 ...
10
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1answer
810 views

Clustered standard errors vs. multilevel modeling?

I've skimmed through several books (Raudenbush & Bryk, Snijders & Bosker, Gelman & Hill, etc.) and several articles (Gelman, Jusko, Primo & Jacobsmeier, etc.), and I still haven't ...
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0answers
14 views

Numpy MemoryError calculating correlation matrix (88k rows / 150 cols) [on hold]

I am getting the following error trying to calculate the correlation matrix on a dataset with 88k rows and 150 columns using Pandas and Numpy. Ultimately I am trying to find associated features on a ...
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1answer
38 views

Multidimensional time series clustering

I have unemployment rates and interest rates per country over time. I want to cluster the countries that have similar dynamics and levels in both dimensions together. What could be a reasonable ...
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0answers
20 views

Is Minimax Linkage a Lance-Williams hierarchical clustering?

I found the following article on "Hierarchical Clustering With Prototypes via Minimax Linkage". It is stated in Property 6 that Minimax linkage cannot be written using Lance–Williams updates. ...
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0answers
12 views

Fuzzy clustering [on hold]

Which distance measure is best to be used with fuzzy clustering of graphs. Somewhere it is written that Euclidean distance measures is not suitable as it unequally weight underlying factor.
0
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1answer
9 views

Characterizing clusters by separate feature vector scores

Say I have a medium amount of dependent variables in a study. These are scores from questionnaires that have been standardized so all are on a scale from 0 to 1. I have clusters of my patients - ...
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0answers
12 views

what is Gower distance? How its a good measure for clustering? [duplicate]

I am bit confused about Gower distance. It can handle all types of data. can someone detail me how?
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0answers
15 views

Using KNN or Clustering techniques to increase data sample size

had a question regarding using KNN or clustering techniques to 'pad' smaller data sets with similar data points. Say, I have some data, a modified and simplified snippet of which looks like this: ...
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0answers
23 views

Random forest clustering

In my data the classes were defined by binning a variable in 10 bins. After growing the random forest its proximity matrix is viewed as the following MDSplot: As can be seen from the plot all ...
1
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2answers
100 views

X-means algorithm and BIC

I want to simulate X-means algorithm based on [1] in MATLAB. I have some questions about this algorithm. X-means Algorithm Steps: (1) Initialize K = Kmin. (2) Run K-means algorithm. (3) FOR k = ...
2
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2answers
150 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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1answer
19 views

Create clusters subject to constraint

I have a set of nodes. Each node represents a building and it has some attributes. For example: x coordinate y coordinate population What I want is to create ...
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0answers
15 views

Using Pelora function in R [on hold]

Although I examined the usage of pelora function which is under supclust library, I can't get why it uses. Is there anyone that explains how it works and what pelora function is ?
5
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1answer
338 views

How do I weight words in title, body text, and links differently in document clustering?

I'm currently trying to play around with NLTK and scikits-learn for text clustering news articles. How do I extend the models to add the scaling of features from a document (I'm doing some ...
95
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6answers
11k views

Why is Euclidean distance not a good metric in high dimensions?

I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
3
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1answer
400 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 ...
0
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1answer
20 views

Outlier detection using clustering on few rows

I have a frequency table (2 columns) of 20 rows of various transaction amounts. Some of these amounts are fraudulent in nature and are pretty obvious as they appear to be outliers in the scatter ...
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3answers
6k 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 ...
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0answers
34 views

MapReduce implementation of Fuzzy K-means [closed]

please help me out here. I needed your help in guiding me about implementing fuzzy k-means clustering algorithm in mapreduce using R langage.
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0answers
19 views

Is it possible to use WEKA in a web based application? [closed]

I am building a web based text mining application. For a word that user enters, the application has to: search it in google gather the documents pre-process by using Bag of words model cluster ...
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0answers
726 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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1answer
15 views

Clustering a list of restaurant dishes

If I have a large list of restaurant dishes that all have the same cuisine... (Pulled Pork, BBQ chicken, 1/2 Ribs, Pork Sliders, Slow Smoked Pork, Full Chicken Special....) What would be a good ...
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0answers
12 views

Clustering without multiple variables

I want to cluster a set of schools according to their academic performance (using the marks of students from each school for a special exam). But the data set only contains the name of each school and ...
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0answers
13 views

DCCA clustering algorithm understanding

I try to understand the step 2.5 of the DCCA clustering algorithm pasted below. The original reference is here and the PowerPoint presentation is here. I have the following questions: Do we perform ...
0
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0answers
11 views

Correlation analysis and Clustering in R - Some suggestions [closed]

I have a biological data, comprising of intensity values associated to 24 masses. Each mass file has 10,000 intensity values corresponding to 10,000 coordinates. So, the data matrix in R would look ...
0
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1answer
30 views

Hierarchical cluster analysis with a similarity matrix [closed]

I am trying to analyse the results from a card sorting study i did, but all i have is raw data. I have the similarity matrix for 53 cards, but I am trying to figure out how to do a hierarchical ...
0
votes
1answer
34 views

k means clustering on sales geolocation data

I have geolocation data (lat and long) per customer per online purchase, and my end goal is to identify common locations per purchase per customer. (basically to see what people typically buy when ...
3
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4answers
98 views

Any easy way to cluster GPS trajectories?

Can anyone recommend an easy way to cluster hundreds of GPS trajectories to find out their common paths? The GPS data is coming from different vehicles that have traveled thousands of miles.
4
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1answer
256 views

Distance between two Gaussian mixtures to evaluate cluster solutions

I'm running a quick simulation to compare different clustering methods, and currently hit a snag trying to evaluate the cluster solutions. I know of various validation metrics (many found in ...
4
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1answer
305 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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4answers
2k views
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2answers
37 views

How much variation should a clustering algorithm explain?

When running a cluster analysis, the algorithm used normally returns a measure of how much variation the clustering explains. e.g. "This clustering explains 96 % of the variation in the data" ...
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0answers
16 views

Appropriate cluster method for 7-point scale data of 58 variables and 635 observations

My research buddy and I are conducting cluster analysis on survey data using a 7-point relevance scale (1=Not relevant, 7=Extremely Relevant). We have 58 variables, arranged in 10 groups of ...
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0answers
25 views

Choosing k in consensus cluster plus using cophenetic correlation coefficient

I am trying choose best k from the consensus clustering using the Cophenetic Correlation Coefficient (CCC). I tried as follows. The correlation coefficients values are poor, i.e., ...
7
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2answers
239 views

Dirichlet Processes for clustering: how to deal with labels?

Q: What is the standard way to cluster data using a Dirichlet Process? When using Gibbs sampling clusters appear and dissapear during the sampling. Besides, we have a identifiability problem since ...
4
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1answer
123 views

Good (2d) visualization of a mixture model clustering

I have a specific problem which I'm surprised I don't find answers on-line and I hope somebody here has a good suggestion for me. I'm working with a large data set which I'm clustering into specific ...
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1answer
119 views

Document image analysis and retrieval with online incremental clustering

Is there any interesting problem in the area of "Document Image Analysis and Retrieval" which by nature needs an online/incremental clustering process ? The problem may be in the context of "Logical ...
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0answers
10 views

Clustering on SVM results?

I have a data set with many subjects. Within each subject, I've run linear SVM to classify two types of stimuli that they see. The decision boundary is different for each participant, as would be ...
4
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1answer
97 views

What type of analysis to choose for this data?

I am trying to create a model of refrigeration having the energy consumption and the temperature over time. So far, I've tried regression but fitting this data into linear model seems impossible. ...
2
votes
1answer
36 views

Benefits of clustering algorithms and Latent Dirichlet Allocation / topic models for finding clusters of words / topics in text

I am interested in finding clusters of words / topics in text. I am trying to learn more about potential approaches. The Wikipedia page on document clustering seems to provide a helpful overview ...
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0answers
5 views

Clustering in R - Clara [migrated]

I just started using R and was trying to cluster with Clara. I am not getting quite the results I had hoped for, and was wondering where I could find the details of the implementation of the algorithm ...
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0answers
16 views

Diagnostic regression for checking the validity of clustering results

I have done an unsupervised non-parametric clustering on sample data gathered by a questionnaire for my thesis (k-means algorithm). A referee asked me to do a diagnostic regression for checking the ...
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0answers
18 views

Clustering algorithm advice for extracting key features in sparse data

I have the following dataset: consider a dataset $X$ of $1400 \times 600$. The rows represent households at time $1 \leq t \leq 14$. So I have $100$ households. The columns represent the programs that ...