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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10 views

Clustering Moving Objects

I'm new on clustering algorithms. I have a data of moving objects generated in time Series. The data generated on equal time interval (1 observation per second). The only information I have about ...
2
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0answers
5 views

How to measure loss of performance of clustering by applying dimensionnality reduction

Let's suppose I have a given dataset whit $n$ features. Having a data-centric approach, I would like to measure the loss of performance of applying a given dimensionnality reduction technique, for a ...
0
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1answer
37 views

k means with binary variables

Is it OK to use kmeans with binary variables? I mean Euclidean distance? I guess the binary variables will be the ones that get the most power to determine the ...
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0answers
13 views

clustering 1d data with a competitive learning method [on hold]

I have a 1D data, and I want to cluster that with any data using competitive learning method. How can I manage this? Thanks,
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2answers
54 views

How to find weights for a dissimiliarity measure

I want to learn (deduce) attribute weights for my dissimilarity measure that I can use for clustering. I have some examples $(a_i,b_i)$ of pairs of objects that are "similar" (should be in the same ...
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2answers
23 views

Clustering from similarity/distance matrix [duplicate]

I have a symmetric and weighted adjacency matrix with $n$ elements. What algorithms exist to cluster the elements from this matrix? The matrix has values between $0$ and $1$. In the case of a ...
0
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1answer
11 views

Why do clustering internal validation indices are decreasing with the number of clusters?

I get the same pattern for 3 different indices: Silhouettes, Dunn and Connectivity- as the number of clusters increases, the score decreases. I am using several clustering methods and several distance ...
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0answers
11 views

How to calculate dissimilarity function between two clusters? [on hold]

I want to write my own code for Dunn's index. If P is dissimilarity measure,then P(min) is fully determined by the pair(x,y) of most closest vectors.(Pattern recognition,Theodoridis & ...
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1answer
18 views

Data representation of text clustering system?

I'm working on a system that should cluster text based data. However, I'm quite new to NLP domain and can't quite get my head around how this data can be represented numerically. Thus assuming I'm ...
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0answers
9 views

how to perform divisive hierarchical clustering

I've been trying for a long time to figure out how to perform (on paper) the divisive hierarchical clustering algorithem, however I'm not able to understand how to do it exactly. example: I need to ...
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0answers
11 views

Where to start with building a system for online clustering of graph based data?

I'm nearly completely new to the field and am looking forward to building a system that can perform online (non-batch, like SGD) clustering of graph based data. Any advises of literature that might ...
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1answer
22 views

Application of PCA in clustering [on hold]

How can I use results of principal component analysis (PCA) from Matlab in a clustering algorithm written in Java? The results of the clustering algorithm are unsatisfactory at higher dimensions, so ...
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0answers
8 views

R: hclust() calculating variance within each cluster

I've got a distmatrix where the element at [i,j] describes the cosine distance between variable i and variable j. When I use hclust(), and then I use cut.tree to make K clusters, then I would like to ...
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19 views

An IR evalualuation metric that only measures the rank of results?

I am working on a little text clustering problem, and trying to figure out how to evaluate the results. I came up with the following idea that I though fits pretty well with the specifics of the ...
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0answers
13 views

Devising a mixed strength cost function for clustering

I'm asking this question with a Computer Vision background (my stat background is limited). I have a set of data that measure the edge strength (based on color gradient) of a set of colors. Since ...
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0answers
12 views

Interpret Kernel Density Estimation for Clustering

I would like to use KDE to cluster 1 dimensional data. For KDE I'm using the code published in MatlabWork ...
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0answers
10 views

Stability of Time Series Hierarchical Clustering

We have a dataset with six time points and three biological replicates each. Therefore, we have a vector of 18 measurements for each feature, and used hierarchical clustering with Euclidean distance ...
2
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1answer
23 views

Need a little help understanding K-means++ seeding

I have been working on a project that involves using K-means clustering for generating adaptive palettes from images. I understand the general process of K-means clustering, and I understand the ...
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1answer
20 views

How to check if the data is intermittent or too many zeros are due to seasonality?

I have a dataset for weekly number of calls to a call center for three years.The data is seasonal (I know this from practitioners knowledge) which means that calls normally come on summer and winter. ...
4
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3answers
61 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 ...
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0answers
20 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 ...
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1answer
24 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 ...
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2answers
18 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 ...
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1answer
24 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 ...
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0answers
27 views

how to divide my categorical data into three clusters [closed]

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 ...
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1answer
28 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 ...
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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 ...
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1answer
11 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 ...
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1answer
26 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 ...
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0answers
10 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 ...
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0answers
23 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 ...
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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 ...
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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 ...
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1answer
17 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 ...
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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
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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 ...
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1answer
39 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 ...
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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 ...
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0answers
19 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 ...
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3answers
32 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
15 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
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 ...
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31 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
44 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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25 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 ...
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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
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7answers
284 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
26 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
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
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1answer
27 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 ...