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
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1answer
33 views

What does it mean for Latent Dirichlet Allocation results to be “good”?

In most paper, Latent Dirichlet Allocation (LDA) model is used for clustering, and the value of $K$ is trained manually (e.g. http://astro.temple.edu/~tua95067/grbovic_cikm.pdf). They claim that this ...
0
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1answer
29 views

SOM dimension doubt

I'm currently working on a research of data clustering using an ANN for self-organizing maps. I'm performing experiments using Matlab, over a Dataset of 20,000 samples and almost 80 variables. The ...
0
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1answer
18 views

Proof that points change clusters less often as iterations proceed in k means

Is there a way that to prove the following: In k-means clustering, as the iterations proceed, the data points tend to stay in their existing clusters, overall, because the replacement of the centroid ...
0
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0answers
43 views

How to detect clustering or related anomalies in cross-section data

I have a cross-section of 100,000 individuals and information on their age. I suspect that there may be clustering by age or that the sample exhibits behavior that there would be two groups, the old ...
0
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0answers
52 views

One dimensional clustering (again!)

I know this question has been asked a lot, but my problem is a lot more specific than those questions, and the solutions provided don't seem to apply. Here's the problem: I have a set of values ...
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0answers
34 views

Generalised Tobit for clustered data (type 2?)

I would like to make a Tobit estimation where my dependent variable is stadium attendance, but there are observations from 18 different stadiums (different capacities). My thoughts are it may be type ...
1
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1answer
22 views

Best Clustering Algorithm for Protein data

I have 400 virus genomes. In each virus, there are 100 genes (these are rough estimates). The genes in these viruses are transferred between each other very frequently. So Gene5 of Virus1 could be ...
1
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0answers
95 views

Cluster prediction of incoming time series(partial)

I have a data set (24 x 1000) (hour x kwh) which contains 1000 time series of a buildings' power consumption, measured every hour. After applying k-means clustering using the dtw criterion I create 5 ...
2
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2answers
76 views

Clustering a correlation matrix

I have a correlation matrix which states how every item is correlated to the other item. Hence for a N items, I already have a N*N correlation matrix. Using this correlation matrix how do I cluster ...
0
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1answer
37 views

Best method to assign new customers to existing clusters after segmentation?

After segmenting customer base using k means algorithm into 5 clusters , how to assign a new customer to one of the existing 5 clusters? Matching just the mean of clusters with values of new ...
0
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0answers
14 views

Online clustering with distances

I'm pretty new to this field so please excuse me if my question sounds naive. I have a stream of distance tuples in the form of (A, B, d) where ...
0
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0answers
14 views

Fuzzy Clusterization and rule etraction

I am trying to do Fuzzy Clusterization and fuzzy rule generation from weather data of different cities worldwide. The goal is cities to be clusterized by the type of climate they have ( tropical, ...
0
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1answer
11 views

Can a 1-D risk score (binary outcome) be sensibly used to create more than 2 treatment groups?

This question concerns predicted probabilities of a binary outcome, and the (I believe) misguided practice of making multiple cutpoints along a one-dimensional risk continuum -- cutpoints that create ...
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2answers
25 views

How to create 2 groups from 1

I have one large group of data and each row which pertains to one animal and its size. So per row I know the size of the animal, here is an example: ...
3
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1answer
116 views

Dummy variables to control for clustering

I have a panel-data sample which is not too large (1,973 observations). The unit of analysis is x (credit cards), which is grouped by y (say, individuals owning different credit cards). I cannot used ...
1
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1answer
20 views

Pick representative element from each cluster

I have made a hierarchical clustering and dived it into a distinct number of clusters. Now from each cluster I would like to pick one element representing the cluster best. What would be a good ...
1
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1answer
95 views

Clustering methods for unknown number of clusters

Matrix $X=[x_1,...,x_i,...,x_N]$ is a data-set containing $N$ data-points that each data-point $x_i$ is a vector of $D$ dimensions. Each dimension is a feature. The number of clusters ($K$) is ...
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0answers
59 views

Hierarchical clustering, linkage methods and dynamic time warping

My goal is to cluster time series based on their DTW distance. Therefore I've calculated full distance matrices as input for several clustering algorithms. I first had a look at hierarchical methods, ...
0
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1answer
30 views

Adaptive Sampling Design

In a pdf, the definition of adaptive sampling design is written as : "An adaptive sampling design is one in which the selection of units to include in the sample depends on ...
1
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1answer
60 views

K-means initial centers membership

I'm trying to plot all the steps of a k-means algorithm with r, but I can't. The k-means algorithm works in this way: Step 1. Initialize the center of the clusters Step 2. Assign the closest ...
1
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2answers
97 views

Clustering based on large Jensen-Shannon Divergence distance matrix

I have a dataset with large number of features and about 15 000 observations. I’m using a probability distribution distance metric related to Jensen-Shannon divergence (JSD) to cluster the ...
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3answers
145 views

Ask for suggestions on clustering methods on a large dataset with mixed types of variables

I need to build segmentation on a large customer dataset with more than 300K records and many variables, including continuous like income and age, ordinal like education level and membership level, ...
0
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1answer
58 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 ...
5
votes
1answer
156 views

k-means|| a.k.a. Scalable K-Means++

Bahman Bahmani et al. introduced k-means||, which is a faster version of k-means++. This algorithm is taken from page 4 of their paper, Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & ...
0
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2answers
58 views

When make clusters in a predictive glm model?

If I want to build a predictive glm model, should I make cluster analysis on 100% of observations or on training sample (80%)? Thanks
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0answers
22 views

Cluster Analysis in splitted database framework

I hope you can help me about my question. I want to build a predictive model, but as first step I need to define some clusters. I split my dataset in two subsets: 80% of observations in the training ...
0
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1answer
38 views

technical issues regarding to cluster analysis

Hi I would like to seek help with my cluster analysis using SAS. The main objective of the task is to segment customers into groups based on their similarity. The dataset contain mixed types of ...
5
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2answers
165 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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4answers
155 views

Is the triangle inequality fulfilled for standard hierarchical clustering distances?

For hierarchical clustering I often see the following two "metrics" (they aren't exactly speaking) for measuring the distance between two random variables $X$ and $Y$: ...
3
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1answer
84 views

Mclust function of mclust package overfitting Gaussians

I'm using the Mclust function of the mclust package in R to fit a mixture of Gaussians model. My simulated data obviously has 3 ...
1
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1answer
59 views
0
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0answers
31 views

Determining k in k-means clustering by community detection in graph

I am faced with a problem of choosing an appropriate number of clusters in highly dimensional data. I've read many approaches to determine the number of clusters, and finally came to a solution and I ...
0
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0answers
52 views

Structural Stability of Hierarchical Clustering

I am interested in some papers and reports about analysing the following problem: Assume, we have a stream of objects and a defined similarity/distance measure to calculate similarity/distance between ...
1
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2answers
45 views

Does clustering need scalar data?

I am trying to cluster 43,000 individuals on about 50 variables. The data contained in the variables are minutes of a radio shows which people listened to in the range of 0 - 3,000,000 minutes. My ...
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0answers
46 views

How to compare and cluster sets of daily time series?

I have multiple dataframes each representing traffic speed for each day of the year (366 dataframes for 366 days of the year). The raws of the dataframe are timestamp from 00:00 to 23:55 at 5 minute ...
2
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1answer
31 views

Clustering Consumer data with over 100 variables and 50000 rows each

I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments. In the past, I've done it using a variety of techniques- ...
0
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0answers
19 views

Problem on clustering

I have around 45 variables in a dataset of 500,000 rows. When I look at my variables, only 10 of them are quantitative - policy premium, age etc. and rest 35 are categorical - State, Smoker/Non ...
2
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1answer
33 views

Using an asymmetric distance matrix for clustering

I'm implementing a clustering task over a precomputed distance matrix. There are several distances I can use for the pair-wise distance matrix, some of them are not a metric (not symmetric). Can ...
2
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1answer
25 views

clustering verifying two basic invariance properties

disclaimer: I already asked something similar on stack overflow, but it seems to be a better place for that question here. I recently became interested in axiomatic definitions of clustering, cf. ...
2
votes
1answer
26 views

Can I use HCA-Ward's cluster-centers to run a K-means including a new item, to see to which cluster is more similar to?

Thank you for reading my question. I have an archaeological case-study, that we can call "Site1", that I want to compare with 9 others "Sites" studied by other scholars. For all of them I have 8 ...
2
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2answers
250 views

Analysis of hierarchical clustered hospital data

I am hoping to get some advice from this excellent community on how I might try to proceed with an analysis of patient outcomes for a large conglomerate of hospitals. Essentially the dataset that I ...
0
votes
1answer
48 views

Determining characteristics of peaks after mclust finite mixture model

I'm working with the mclust package in R (specifically using densityMclust). As output, I have a file with mixing probabilities, variances, and means for each ...
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2answers
43 views

Which K-mean algorithm I have to use for this problem?

Perform a k-means Clustering (non-iterative algorithm) using k=2 randomly initialised centroids (cluster prototypes), and the Euclidean distance. At the moment I manage to understand you can use ...
1
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1answer
13 views

Separating one group from other samples where the other samples may not belong to the same population

The way I see it, this is somewhat of a modified clustering problem. Let's say I have 1000 samples where the majority all follow the same behavior since they are from the same population. A number of ...
0
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1answer
22 views

Comparing / quantifying clusters

I have 'n' observations which are classified in two classes: Class A and Class B. The observations are mis-balanced with Class A constituting around 90% of the samples and Class B around 10%. The ...
0
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0answers
19 views

Fuzzy clustering of temporal data with constraints

I am looking for an algorithm to cluster timestamped events using the weighted elapsed time between the events as the only dimension. There are different types of events. There are constraints saying ...
84
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2answers
6k views

How to understand the drawbacks of K-means

K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a data set and a pre-specified number of clusters, k, then I just ...
0
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0answers
41 views

Gower's Distance issue

So, I'm relatively new to using Gower's distance to do cluster analysis. I've done some research on this for a little while and like the fact it can incorporate categorical variables. To get a better ...
0
votes
1answer
48 views

Learning a classifier to compute distance between points for clustering

I have dataset of items and want to cluster them. However, I don't have a predefined distance function. Does it make sense to learn a classifier that can predict the similarity between any two items? ...
0
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0answers
14 views

Merging linkage results from separate datasets for the purpose of clustering

Suppose I have a dataset P, This dataset is separated into smaller disjoint subsets Q_i. Since the clustering algorithm which has to be applied is space O(n2), the amount of data in P such that it ...