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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38 views

When does the EM for Gaussian mixture model has one of the Gaussian diminish to exactly one point and have zero variance?

I had implemented the EM algorithm for mixture models as follows: For the E-step I compute the soft-counts of assigning each point $x^{(t)} \in Data_n$ to an individual cluster $j \in \{1, ..., K \}$ ...
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0answers
22 views

Avg,max dissimilarity,isolation=0 for certain clusters after using Clara() on R

I found the best k value after running a silhouette test to get k=21. On running clara() on the dataset of 13805 points, I found a pretty interesting trend: Non-zero memberships, but zero values of ...
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1answer
28 views

Clustering of sequential data

Given the following scenario, I have a really long street. Each house on the street has some number of children. If I were to sequentially append the number of children in each house along an array, I ...
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0answers
36 views

How do you do EM algorithm for a factored model for a recommender system?

Let $X$ be a $n \times d$ matrix with users as rows and movies as columns. Each user is a single row $x^{(u)} \in \mathbb{R}^d$ (i.e. for user u there are at most d ratings for the d movies). Also ...
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1answer
86 views

What is the benefit of using Manhattan distance for K-medoid than using Euclidean distance?

Please give me the reasons. I didn't find any k-medoid example that's calculation is done using Euclidean distance. All examples are made of Manhattan distance for k-medoid.
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0answers
47 views

pattern recognition or clustering for analyzing seasonal data

I have a set of historical data for an event which is highly seasonal. The event can be held in spring and summer but it is not planned for fall and winter. I wanted to forecast days to the next event ...
3
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7answers
355 views

Comparing k-means results to original data: how to interpret the resulting plots?

I'm running k-means on my dataset that can be found here that has 7 classes. I plotted the ggpairs for the dataset and then took k-means and plotted ggpairs again ...
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0answers
44 views

K-means validation

If anyone knows a suitable approach to validate cluster solution, I will be glad if the person share with me. I am conducting a research using k-means and partition gave me two groups. The second part ...
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0answers
13 views

Finding the features that have meaning in subset of data

I have a set of $N$ points $x_i=(x_i^1, x_i^2,...,x_i^{m+k})$ in $m+k$-dimensional space ($m$ continuous dimensions and $k$ discrete). Also I have a subset of these points that are marked as "bad". ...
1
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1answer
58 views

Why do final cluster centers change after applying results from past K-Means clustering (SPSS)?

I have a question regarding what happens after I apply k-means clustering centers to a new data set. Basically, I ran k-means clustering on a dataset1, saved the cluster centers, and applied it to a ...
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0answers
10 views

Ordination or Clustering method using layered data

I need a clustering method that makes use of layered data. I have around 50 random sampling points from a surface. Each point samples layers below that point. Imaging geological layers or water layers ...
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0answers
52 views

How many clusters would you divide this dendrogram into?

I'm struggling as to where to cut this dendrogram: any help would be appreciated. Thanks
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0answers
29 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
61 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 ...
0
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1answer
21 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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1answer
48 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 ...
1
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0answers
59 views

Gaussian mixture models restriction? [closed]

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 $\Sigma_i$ ...
7
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1answer
411 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
20 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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1answer
29 views

FAMES 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
56 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
74 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
22 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
36 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
37 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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1answer
46 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 ...
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1answer
12 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
24 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
57 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
64 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 ...
0
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0answers
36 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 ...
0
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1answer
19 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
47 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
233 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
46 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
16 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 ...
0
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0answers
41 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
45 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
30 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
152 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
48 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} ...
3
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1answer
170 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?
1
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1answer
64 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 ...
0
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0answers
15 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?
14
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2answers
723 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 ...
1
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0answers
12 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
130 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
19 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 ...
0
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0answers
31 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 ...