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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3
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2answers
37 views

Can you run clustering algorithms on perfectly collinear data?

Let's say I have the data set $x_i,y_i,z_i$, where $z_i=y_i-x_i$ or $z_i=f(x_i,y_i)$. Can I run clustering algorithms on this data set? I wanted to add non-linear or linear combinations of variables ...
0
votes
0answers
18 views

Clustering data with one feature

Is there any built in method to cluster data with one categorical dimension in R? Basically, I have a data set including week of the year and if an event happened in that week. I wanted to use ...
-1
votes
1answer
18 views

What are the benefits for semi-supervised learning over unsupervised clustering? Or any limitations?

I have another question about semi-supervised learning vs unsupervised clustering, what are the benefits and limitations? I have got some data with labels and some without labels. I performed ...
1
vote
1answer
12 views

Line that separates data partitioned by the first principal component of PCA

I want to partition some 2d points into 2 groups (clustering). The way that I need to do it is by using PCA to find the first principle component. Then I project the data to find 1d projections. Then ...
1
vote
1answer
17 views

Clustering data points based on edge strength

I'm looking at a Computer Vision application where I try to analyze the strength of edges a certain set of colors make with another color. For, this I take images of two colors falling on top of each ...
-1
votes
0answers
19 views

how to divide my categorical data into three clusters [on hold]

I have some data of 10 attributes, all of which are categorical attributes (factors in R). I would like to divide this data into three homogeneous sets(clusters). I went about doing 3-means ...
1
vote
1answer
25 views

A proof of total sum of squares being equal to within-cluster sum of squares and between cluster sum of squares? [duplicate]

In cluster analysis I have frequently encountered a statement that the total sum of squares $\sum\limits_{i = 1}^n {{{({x_i} - \overline x )}^2}} $ being equal to within-cluster sum of squares ...
1
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0answers
25 views

How to estimate the the optimal number of clusters in a dataset by Clustering quality measures? [duplicate]

How to estimate the the optimal number of clusters in a dataset by Clustering quality measures? I have four datasets iris,breast cancer, magic ,wine and yeast. All the datasets are taken from UCI ...
1
vote
1answer
9 views

Can I do cluster analysis of dyadic data?

I have multilevel data that is dyadic in the unit of observation. The dyad is a unique pair of countries that sign a treaty, such that no dyad repeats itself. For example, the US-UK treaty, the ...
0
votes
1answer
24 views

Chosing optimal k and optimal distance-metric for k-means [duplicate]

I have a data-set with roughly 20-dimensions and millions of points which I want to cluster. The goal is to find a set of clusters which: Are as distinct as possible from each other (minimum ...
0
votes
0answers
5 views

Likelihood ratio test to choose between components of gaussian mixture model?

I have a Gaussian Mixture Model with 2 components. Is it possible to use a likelihood ratio test to determine the point at which the probability of being in component A is the same as being in ...
-1
votes
0answers
20 views

Clustering binary letters/text - image features and descriptors

I have a task to cluster 1 000 000 images (where each image represents one letter). They are all in binary format. I need to get clusters where each of them would represent 1 letter.. Which image ...
-1
votes
0answers
21 views

Data mining - Determining number of clusters via silhouette vs. number of clusters plot [closed]

I am using R to try and figure out the optimum number of clusters within a data set. I want to plot two graphs: 1) the within sum of squares vs. the number of clusters and 2) the average silhouette ...
0
votes
0answers
13 views

What is the best approach to extract keywords from lots of document?

I have many documents, let's say ten thousands or more. Each document talks about a unique topic and I'd like to extract some keywords (let's say 5 keywords) from each document. As Latent Dirichlet ...
1
vote
0answers
20 views

before clusterisation, should I remove observations with too few measurements?

I have a very unevenly distributed dataset of 462 twitter users. During the window of observation, some of these users have produced as many as 2000 tweets, while others as few as one. My end is to ...
-1
votes
0answers
33 views

Nearest algorithm according to which the humans analyze the data [closed]

Which Algorithm analyze the data just like the people does? Nearest algorithm according to which the humans analyze the data Can I say that the people group the data similar to the s.link algorithm ...
-1
votes
1answer
16 views

Multivariate grouping - how to cluster/group elements with three attributes [closed]

I have three dimensional attributes: height, breadth, length for a large number of elements. I want to simply form groups of these elements based on these three variables, where I can further test ...
-1
votes
0answers
13 views

misclassification rate in clusters [duplicate]

Hi I am trying to group 40 column vectors into 4 clusters using k-means. how do I compute the misclassification rate of these clusters
0
votes
0answers
7 views

F statistics is given as dots after using clustered standard errors option

My question is the following: I am using Demographic and Health Survey of Turkey to estimate the equation below. Standard errors are clustered for 26 regions, in which individuals lived when they ...
0
votes
1answer
36 views

How can Markov cluster algorithms be used to cluster strings?

I have just start learning about Machine Learning and while surfing on the web, I saw that another CV user in those post has offered Markov cluster algorithms to cluster long strings. As far as I ...
-1
votes
0answers
25 views

How to calculate silhouette coefficient of a single cluster?

I use silhouette coefficient in order to choose the best "k" for k-means algorithm. I implemented my function that, given a set of clusters, calculates this coefficient. My question is: how can I ...
0
votes
0answers
18 views

advice regarding which ant clustering algorithm to choose

I am working on this project in which I am going to take a small corpora of input text consisting of works of literature from different genre. After extracting a set of features, I wish to perform ...
1
vote
3answers
29 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 $j \in \{1, ..., K \}$ ...
0
votes
0answers
12 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 ...
0
votes
1answer
20 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 ...
1
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0answers
30 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 ...
0
votes
1answer
43 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.
0
votes
0answers
20 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 ...
-1
votes
0answers
37 views

Clustering algorithm when we already know what sort of clusters should be formed?

Normal clustering algorithms like k means are given no information about what sort of clusters are to be formed. However, I am looking for a clustering algorithm which will cluster into groups that I ...
3
votes
7answers
280 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 ...
1
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0answers
25 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 ...
0
votes
0answers
9 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
vote
1answer
25 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 ...
0
votes
0answers
9 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 ...
1
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0answers
42 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
0
votes
0answers
26 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
votes
1answer
33 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
votes
1answer
14 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
votes
1answer
31 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
vote
0answers
51 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 $\Sigma_i$ ...
6
votes
1answer
102 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 ...
0
votes
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
votes
1answer
18 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
votes
1answer
53 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
votes
0answers
39 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
votes
0answers
17 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 ...
0
votes
0answers
32 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. ...
-1
votes
2answers
26 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 ...
0
votes
1answer
32 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
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 ...