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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46 views

Interpreting hierachchical cluster output

This is a dendrogram resulting from a hierarchical clustering using SPSS. I thought the clustering is done in the following way. I would like to know if the way I am interpreting is correct. ...
8
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1answer
177 views

Clustering — Intuition behind Kleinberg's Impossibility Theorem

I've been thinking about writing a blog post on this interesting analysis by Kleinberg (2002) that explores the difficulty of clustering. Kleinberg outlines three seemingly intuitive desiderata for a ...
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1answer
44 views

TSS returned by K means clustering is always the same

I have high dimensional ($m \approx 2k$), high sample (n=140,000) dataset in R that I load into memory run PCA on it (returns $m \approx 400$ components to cover 95% of variance) then I run k means ...
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1answer
20 views

Derive conditions on features from clustering

disclaimer: I'm a noob. So bear with me because I don't even know the right terms to search waht I'm looking for. I have this problem: I do cluster a dataset with all numeric features. I want to ...
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0answers
15 views

mixture of 2 Gaussians and using a priori information about one of the Gaussians

I am working on a large dataset of 2 populations, one is healthy controls and other is considered to be dysfunctional My variables interests suggest a good fit for a unitary Gaussian distribution for ...
3
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0answers
97 views

Highly Connected Subgraphs cluster

i'm studying a method to cluster similar topic represented in a graph like this: The result must be: [0] = 1,2,4 [1] = 3 I tried Markov Cluster Algorithm but ...
2
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1answer
23 views

Should one create interaction terms when clustering

I have a large set of feature vectors (>10^6) I would like to cluster. Each vector has only 4 numerical features. However I know they are correlated. Should I create interaction terms before ...
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1answer
47 views

The optimal number of cluster by Gap Statistics

I'm using the GAP statistics (clusGAP) to find the optimal number of clusters in my gene expression data. But I'm not sure whether the optimal number suggested by clusGAP is right or not. I ran the ...
4
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1answer
167 views

Clustering a dense dataset

Problem: I am figuring out the best way to find clusters for a dataset with observations that are densely packed together. The dataset is retail stores with three numeric variables based on ...
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2answers
527 views

Etymology of “cluster” in the context of cluster analysis

I'm trying to track down the origins of the word "cluster" and its usage in the context of cluster analysis. Please, does anyone know when and by whom it was first used? Perhaps there was a paper or ...
2
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2answers
41 views

In clustering, how does Normalized Euclidean Distance represent the number of standard deviations from a cluster?

In clustering, one has to choose a distance metric. I've seen Normalized Euclidean Distance used for two reasons: 1) Because it scales by the variance. 2) Because it quantifies the distance in ...
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1answer
50 views

Looking for fancy industrial applications of clustering

I'm going to supervise a high school student project. The goal is to have them discover data mining by "reinventing" the K-means algorithm and, eventually, going farther than the basic K-means. The ...
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23 views

Setting a cutoff based on a bimodal distribution

For context, I am working with high dimensional data, where a certain fraction of features is used to derive clusters. Others, when doing similar analyses, have defined the fraction of genes they use ...
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2answers
226 views

Comparing & clustering time series with unequal lengths

I wish to compare and cluster 10 time-series with different lengths. What are the techniques that I can used and do they have implementation in R? I found that dynamic time wrapping is nice but does ...
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1answer
29 views

Clustering experiments based on event distance matricies

I'm running a bunch of experiments with randomly picked "knobs", and I'm recording various event types and times they occurred during the event. I'm particularly interested in getting a good variety ...
0
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1answer
68 views

graph clustering algorithm - Markov MCL?

i have of set of document with this correlation (based on similarity) This is the set: 1: 2,3,4 2: 1,4 3: 1 4: 1,2 Now i start creating a matrix ...
2
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0answers
48 views

Best practices for reducing/clustering data which is location/time based?

I have event-based data (or longitudinal data) which has a person's age, gender, location (zipcode etc.), the datetime when they saw something, and how long they watched it. I have a project ...
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0answers
15 views

Measuring salient changes in 2 Dimensional clusters using using existing functions in Matlab

I have a series of frames where I want to analyse changes in spatial distributions of points. I have a list of x,y values or 2D coordinates measuring changes in particle distribution. As shown in ...
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0answers
29 views

Classifier from clustering data with envelope/convex hull

I have been searching for any data classification algorithms that have the following criteria but have yet to find any. Any suggestions on places to look or implementations would be appreciated: 1) ...
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0answers
16 views

Rescale data in subtractive clustering into [0,1] interval

I read here http://www.sciencej.com/keshavarzi_3_1_2012_27_33.pdf (page 4) and here https://www.academia.edu/3691440/Modeling_Academic_Performance_Evaluation_Using_Subtractive_Clustering_Approach ...
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1answer
44 views

Clustering structured data

Does clustering make sense at structured data? I have a sales data set from a retail company. The data is structured into Product Lines, Product Groups and Colors and Articles. I wanted to make a ...
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1answer
37 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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0answers
33 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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1answer
60 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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35 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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1answer
74 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 ...
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1answer
81 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 ...
0
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1answer
14 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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1answer
32 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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2answers
420 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 = ...
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0answers
16 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
16 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 ...
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1answer
19 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 ...
0
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1answer
78 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 ...
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0answers
22 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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2answers
48 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
72 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., ...
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0answers
17 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 ...
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0answers
21 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
35 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 ...
0
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1answer
122 views

Cluster Analysis on GPS data - Assigning GPS coordinates to core groups

I'm trying to figure out a way to assign GPS coordinates to core GPS values. For example, I've got tons of store locations (with long & lat coordinates) and I'd like to group them to one of x ...
2
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0answers
234 views

R: silhouette with k-means

I'm currently checking some clustering evaluation indexes in R, and now I'm using Silhouette and its respective function in R, "silhouette" (in "cluster" package). To test the method, I used the ...
4
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1answer
103 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. ...
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0answers
17 views

Finding influential characteristics in a chain of events

I have some data which is sequences of actions performed by individuals. All of these actions have properties (some catagorical, some binary, some continuous numeric). Individuals can have 1 to ...
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0answers
25 views

Statistic to measure grouping (or intensity) of observations

I'm looking for a statistical technique that can measure the level of grouping or intensity of observations. I'm not sure what the proper terminology is, so I will try to explain my question through ...
1
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4answers
77 views

How do you cluster data such that each cluster satisfies a condition (such as “diameter of each cluster can't be larger than 10”)?

I would like to run some kind of clustering algorithm on my data (which can be thought of as a collection of vectors). I do not want to start with using k-means because I do not know what ...
0
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1answer
25 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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0answers
21 views

Understanding divisive clustering code

I came across divisive clustering algorithm (Cichosz, P. (2015) Data mining algorithms: Explained using R; page 262), which is implemented in R. The appropriate function is pasted below. Actually, I ...
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0answers
29 views

Gower Distance, Ordinal Variable, R, Error?

I am trying to implement the CritCF function from http://www.sciencedirect.com/science/article/pii/S0031320310004905 for feature selection in clustering. I need to compute the distance from cluster ...
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4answers
123 views

Classify users by the pages they liked

I have a data set of users, and a list of pages each users liked. My goal is to derive k classes of users. The first thing that comes to mind are bag-of-words ...