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 views

some inference about k-NN algorithms for better understanding?

I ran into some facts make me confusing. for k-NN classifier: I) why classification accuracy is not better with large values of k. II) the decision boundary is not smoother with smaller ...
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0answers
26 views

Gaussian mixture models restriction? [on hold]

I read this note that with striction on GMM with some condition this algorithm is more like to K-means: the adaptations of the Gaussian mixture models algorithms with Restrict each $\Sigma_i$ ...
5
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16 views

Jenks Natural Breaks in Python: How to find the optimum number of breaks?

I found this Python implementation of the Jenks Natural Breaks algorithm and I could make it run on my Windows 7 machine. It is pretty fast and it finds the breaks in few time, considering the size of ...
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1answer
16 views

Significance of a cluster with respect to the whole dataset

I mainly am a machine learning guy and my knowledge of statistics is a bit limited. I have a (small) dataset of objects described by two categorical variables: $$ \begin{array}{|l|l|l|l|l|} \hline ...
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13 views

which package in R perform k-prototype algorithm? [on hold]

I have mixture of nominal and discrete numerical data.I want to cluster the data and as i know some clustering algorithms such as k-means are not appropriate for clustering mixed data.So i want to ...
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6 views

Identifying Reoccuring Transactions [on hold]

What methodology(ies) would be best for identifying reoccurring transactions within an account if the only data one had on the transactions were transaction amount, date, and a categorical source ...
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0answers
17 views

clustering evaluation with mixed data [on hold]

I want to do clustering and evaluating the clusters validity. I have mixed data(mixture of nominal and discrete numerical data). I used gower's coefficient in standard hierarchical clustering and ...
0
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1answer
13 views

Triangular geometry in case of Dynamic Time Warping

I found this paper Using Pivots to Speed-Up k-Medoids Clustering in which authors explain how to use triangular geometry and cosine law to speed up search of new medoids in case of K-medoids. My ...
2
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1answer
51 views

Clustering Matrices

Suppose I have a set of 100 $n \times 2$ matrices that all have the following format: Bid Profit [5.00 7.10] [3.14 6.04] [2.9 10.08] Where the numbers ...
2
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0answers
27 views

jenks natural breaks vs k-means

I am new to this topic. As far as I know both are data clustering methods. Then my question is when is Jenks prefered over k-means? I read on this website that jenks is particularly suited for ...
2
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0answers
14 views

Cluster Assignment in Bayesian perspective

I am going to study clustering methods in the Bayesian perspective. I understood how k-means works, and I found it pretty clear, due to the notion of distance and assignments to specific centers. I ...
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0answers
29 views

How to generate multidimensional data with specific clustering properties?

In section 5.A of a research paper the researcher used the following synthetic datasets: GAUSS consisted of six Gaussian clusters with identity covariance, each with 500 points in five dimensions. ...
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2answers
17 views

k-means nstart equivalent for EM Clustering? Report only the best solution from a large number of initializations?

In K-means clustering, you can specify an nstart=i parameter, which performs the algorithm i times (i.e. selects the initial k random centroids i times) sand reports the best answer only. If I perform ...
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0answers
24 views

k-prototype clustering with mixed data [closed]

I know that numerical clustering algorithms such as k-means are not suitable for clustering mixed data (nominal and numerical data), so I want to use k-prototype clustering in R.I have 1 discrete ...
0
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1answer
29 views

Finding natural groups / clusters in an undirected graph / over several undirected graphs

What kind of methods are there to find natural groups or clusters within an undirected graph structure? I am new to graph theory, but the project seems to have confronted me with questions that could ...
0
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1answer
10 views

Clustering with summarised variables

I have a total of 32 observation that I would like to cluster. The data avaiolable comes in 2 formats: Continuous data Summarized categorical data What I mean with summarized categorical data is ...
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1answer
10 views

Statistical tests for a binary response variable with more than 2 repeated measures?

Question: Did plants experiencing treatment A vs. treatment B receive significantly more or less observations with zero visits from pollinators? Methods: Plants were clustered by similar physical ...
0
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1answer
12 views

K-Medoids swapping inside clusters

I'm a bit confused with concept of K-medoids. It seems that original algorithm (PAM) describes that swap step should be performed by swaping only one of the medoids with one non-medoid point from ...
3
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1answer
53 views

Ridge regression in multivariate Gaussian distribution

When implementing GMM (Gaussian Mixture Model) in practice, the covariance matrix ${\Sigma}_{D\times D}$ is often singular. The reason is that we have to estimate $\frac{D(D+1)}{2}$ parameters in ...
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0answers
21 views

Can a classification algorithm be used to measure the Clustering Quality (CQM)

Can a classifier be used to measure the Clustering Quality measure? I have come across this paper where the researcher uses a classifier like : five nearest neighbor classifier, a C4.5 decision ...
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1answer
17 views

Semistructured document classification

I am trying to cluster products based on the text descriptions of the products. I have millions of products. The nature of the products could be hierarchical. i.e; Clothing will have T-Shirts & ...
0
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1answer
25 views

K-means++ like initialization for K-medoids

Does it make sense to use initialization in K-medoids like in the case of K-means++? To be precise - is it good to select "farthest" points as initial medoids? (farthest in sense that points that are ...
4
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2answers
200 views

Can humans cluster data sets manually? [closed]

Can human cluster data sets manually? For example, consider the Iris data set, depicted below: Instead of using clustering algorithms like connectivity-based clustering (hierarchical clustering), ...
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0answers
33 views

Consensus methods for interpreting results of a cluster analysis

With cluster analysis, sometimes we want to assign meaning to the nature of the identified clusters. This is subjective and often done by a single person (or very few people) with perhaps some ...
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0answers
12 views

Which clustering method should I use and other useful statistical tools for grouping

I have 37 plant species (rows) and up to 18 corresponding traits (col). I would like to see if there are consistent species-wide attributes that are repeated in different species and can therefore be ...
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0answers
24 views

Using PCA to find most 'similar' points to a given observation (mixed data)

I am trying to find the most 'similar' points to each other in a dataset of mixed data. I understand that if these were all numeric variables on the same scale, one could simply use Euclidean Distance ...
1
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1answer
27 views

how to determine medoids based on (dis)similarity matrix

Given the (dis)similarity matrix and the clustering results, how do I select the medoid in each cluster? For example, one cluster contains totally 4 points: A, B, C, D. I know the similarity (or ...
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0answers
27 views

How many samples are enough

I have objects with large number of attributes (about 60.000). Attributes are actually deviations of object part from model. I would like to cluster this objects, to get centroids that will represent ...
3
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1answer
50 views

Understanding cluster plot and component variability

I have run k-means clustering. I have also plotted the results using the following code in R: ...
1
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1answer
36 views

Clustering singular vectors [closed]

I have a $n\times m$ matrix $A$ that I have done SVD on and picked off $k$ out of $n$ highest singular values and vectors. That is, I have decomposition for the $k$ rank approximation of $A$: $A^{k} ...
1
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1answer
41 views

Difference between K-medoids and PAM

I understood that PAM is just one kind of K-medoids algorithm. The difference is in new medoid selection (per iteration): K-medoids selects object that is closest to the medoid as a next medoid PAM ...
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0answers
15 views

How to divide scattered points among multiple curves? [duplicate]

I have the following scattered points graph: To me, it seems that the points describe 2 (maybe 3) logarithmic curves. Is there a way to say which point belongs to which curve?
0
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1answer
21 views

filling out NA values using clustering analysis

I have a data frame with a large number of NA values. I do not wish to leave out all these rows as that would reduce the size of my training set drastically. I filled out these missing values in a ...
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0answers
10 views

What does choice of metric say about the data?

I am using KNN method. I have three choices of metric (Euclidean, Manhattan and Chebyshev). The Error rate was minimum when I used Chebyshev distance. What does it say about the distribution data?
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2answers
366 views

Why does gap statistic for k-means suggest one cluster, even though there are obviously two of them?

I am using K-means to cluster my data and was looking for a way to suggest an "optimal" cluster number. Gap statistics seems to be a common way to find a good cluster number. For some reason it ...
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0answers
10 views

Adapting clustering to partial clutering

This is a question primarily about re-appropriating existing clustering methods to work when only a fraction of datapoints are likely to originate from a cluster (as opposed to a global background ...
4
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1answer
42 views

Within-group sum of squares of cluster

I have a multivariate dataset for which I have only a table including the cross-wise Euclidean distances between all points and a list giving the assignment of each point to one of several clusters. ...
0
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0answers
16 views

Clustering objects with missing values

I have some time-series that I would like to cluster, but they can have missing values. One approach that may be ad hoc is to use an algorithm like K-medoids, and to use similarity measure that will ...
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0answers
22 views

clustering discrete data - how to get “autocorrelative distance” matrix?

I'm trying to cluster discrete (histogram) data with unequal bins. I came across the post: Clustering distributions and calculated the cumulative sums of each data set and interpolated between ...
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0answers
10 views

Reveal confusing class blocs in a large confusion matrix

I built a linear classifier for 85 classes. When I get predictions, I construct a confusion matrix. If I visualize it, it looks pretty noisy. I would like to reorder rows and columns such that I ...
2
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1answer
34 views

Unsupervised Clustering using randomForest

Outline of clustering technique using Random Forest A synthetic data is created by randomly sampling from the data of interest. It is used as the base line to measure the "structureness" or ...
3
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1answer
123 views

What is the difference between graphs/networks? [closed]

Note: read down to below "Question" to find the question. Background: In a previous question I asked how to group what I would call nodes on a network graph based on a connectivity matrix. (link) ...
0
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1answer
41 views

interactive clustering of a 3D point cloud by changing the granularity

I want to cluster a point cloud in a 3D space (maybe with 200k points). For this I'm locking for a botom up approach. My goal is, that I can change the granularity of the clustering with a ...
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0answers
23 views

How to categorize users based on their movie views?

Apologies for cross posting. I have a dataset of size (61573, 25). The rows represent users whereas the columns represent ...
1
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2answers
135 views

How can an artificial neural network ANN, be used for unsupervised clustering?

I understand how an artificial neural network (ANN), can be trained in a supervised manner using backpropogation to improve the fitting by decreasing the error in ...
0
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0answers
13 views

Determining clustering words

I'm looking for an alternative to PMI for the following problem: I have a set of $n$ classes of text corpuses, and I'm trying to find the keywords that differentiate the corpuses from each other. For ...
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0answers
9 views

How to make clustering / segmentation on date to percentage intervals?

I've got a table with 3 columns: query, ctr and mean position I want to identify all the cases where the CTR is low while the mean position is good. First of all I need to segment the continuous CTR ...
1
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0answers
27 views

How can we say that a clustering quality measure is good?

There are few well known measures like silhouette width (SW), the Davies- Bouldin index (DB), the Calinski-Harabasz index (CH), and the Dunn index . How can we say that a clustering quality measure ...
0
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1answer
19 views

Finding words belonging to a topic

Consider forum posts or any text where we'd be interested in finding out related words, given the data. What would be a solution for creating a topic cluster based on this data? E.g. We are interested ...
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2answers
111 views

Approach and example of graph clustering in “R”

I am looking to group/merge nodes in a graph using graph clustering in 'r'. Here is a stunningly toy variation of my problem. There are two "clusters" There is a "bridge" connecting the clusters ...