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
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2answers
1k views

Fuzzy K-means - Cluster Sizes

I'm trying to do fuzzy k-means clustering on a dataset using the cmeans function (R) . The problem Im facing is that the sizes of clusters are not as I would like them to be. This is done by ...
0
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0answers
7 views

Clustering on this reinforcement learning approach?

I am trying to create an agent that selects an action depending on a state that gives back maximum reward. To keep things simple I will keep it to two actions and 24 different states. The states is ...
0
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1answer
19 views

Probability for selecting centroids - K-means++

K-means++ selects centroids one by one, where each point has the chance to become next centroid with probability proportional to distance to closest centroid already selected. I implemented it like ...
1
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1answer
21 views

VC dimension of a learner is N?

I ran into a challenging question. Which of the following procedures is sufficient and necessary and most efficient for proving that the VC dimension of a learner is N? Show that the ...
6
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1answer
52 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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0answers
27 views
0
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1answer
12 views

Hierarchical clustering methods using a similarity metric for which d(x, x) != 0, and possibly assymmetric

I want to cluster files based on an information distance, which is obtained by comparing the compressed length of two files separately and the concatenation of the two files, using a real-world ...
2
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0answers
17 views

Cluster analysis without knowing the structure of the data set

I’m working on a task regarding cluster analysis for about half a year now, but since the fields of pattern recognition and cluster analysis are quite complex ones, I would call myself a beginner in ...
3
votes
1answer
56 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 ...
2
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1answer
93 views

Detecting strong currents in a sparse directed graph

I have a very large, sparse, weighted, directed graph. The structure is such that it mainly consists of strings of nodes connected with highly weighted edges. These strings can be connected by weak ...
1
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1answer
112 views

Motivations for Shi-Malik Algorithm

So I've been trying to make sense of the clustering algorithm on page 6 of this paper. Are the "first" k eigenvalues they refer to the smallest eigenvalues? What are the $y_i$ exactly? I don't ...
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0answers
40 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 $\Sigma_i$ ...
0
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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 ...
0
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1answer
27 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 ...
0
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1answer
15 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 ...
1
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1answer
97 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 ...
3
votes
4answers
1k views

Rand index calculation

I'm trying to figure out how to calculate the Rand Index of a cluster algorithm, but I'm stuck at the point how to calculate the true and false negatives. At the moment I'm using the example from the ...
8
votes
2answers
575 views

What is the intuition behind the variation of information (VI) metric for cluster validation?

For non-statisticians like me, it is very difficult to capture the idea of VI metric (variation of information) even after reading the relevant paper by Marina ...
3
votes
2answers
421 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. ...
0
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1answer
450 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 ...
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 ...
-1
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0answers
14 views

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

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 ...
0
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0answers
7 views

Identifying Reoccuring Transactions [closed]

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 ...
-1
votes
0answers
17 views

clustering evaluation with mixed data [closed]

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 ...
-1
votes
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
votes
1answer
705 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) ...
-1
votes
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 ...
-1
votes
1answer
88 views

R - how to transform the similarity matrix to distance matrix for performing hierarchical clustering?

I am trying to cluster nodes (C1, C2, C3...) of a graph using hclust and my similarity metric is number of links between nodes. I have data like ...
6
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0answers
731 views

How do I algorithmically determine values of T1 & T2 for canopy clustering?

I am trying to use canopy clustering to provide initial clusters for KMeans in mahout. Is there a way to determine / approximate the values of the distance thresholds T1 & T2 algorithmically? ...
2
votes
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 ...
0
votes
1answer
308 views

why Hierarchical Clustering pvclust vs. hclust got different result?

I am performing the hierarchical clustering analysis on a dataset of 25 viral populations using 3 viral components (variables) to construct a dendrogram with average method and correlation distance ...
2
votes
0answers
28 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 ...
2
votes
2answers
133 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. ...
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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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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1answer
128 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 ...
2
votes
1answer
123 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 ...
1
vote
1answer
204 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. ...
1
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2answers
294 views

Clustering a long list of strings (words) into similarity groups

I have the following problem at hand: I have a very long list of words, possibly names, surnames, etc. I need to cluster this word list, such that similar words, for example words with similar edit ...
4
votes
1answer
248 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 ...
2
votes
2answers
126 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 ...
2
votes
1answer
37 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 ...
0
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2answers
126 views

Proper Statistical Test for Binary Data

I looking for the best statistical test to apply in a particular situation and I hope I can find here the answer(s) I'm looking for. First of all some details: I'm studying 33 different mutants of a ...
6
votes
3answers
380 views

Can you compare different clustering methods on a dataset with no ground truth by cross-validation?

Currently, I am trying to analyze a text document dataset that has no ground truth. I was told that you can use k-fold cross validation to compare different clustering methods. However, the examples I ...
4
votes
1answer
232 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 ...
0
votes
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 ...
3
votes
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 ...
1
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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 ...
4
votes
3answers
849 views

Cluster analysis on panel data

I have a panel data set (country and year) on which I would like to run a cluster analysis by country. My data set has around 20 variables. Here's a summary for my panel data: ...