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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1answer
19 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 ...
3
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2answers
447 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. ...
1
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1answer
223 views

Cophenetic distance matrix to a dendrogram

In hierarchical clustering procedure, a distance matrix is used to construct a dendrogram with an appropriate method of clustering. In the process of constructing a dendrogram, a cophenetic matrix is ...
-1
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0answers
10 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,
0
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1answer
10 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 ...
5
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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 ...
0
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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 ...
2
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1answer
140 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. ...
1
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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 ...
-1
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0answers
10 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 & ...
0
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1answer
790 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) ...
0
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0answers
7 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 ...
0
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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 ...
5
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1answer
101 views

Co-occurrence of properties in a population

I have 150 properties that may occur in a population of 10000 people. Individual people may have none, one or a couple of these properties. The properties are not mutually exclusive and have different ...
0
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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 ...
1
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1answer
129 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
149 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 ...
0
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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 ...
1
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2answers
2k views

How to perform K-medoids when having the distance matrix

I've been trying for a long time to figure out how to perform (on paper)the K-medoids algorithm, however I'm not able to understand how to begin and iterate. for example: I have the distance matrix ...
0
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0answers
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 ...
0
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1answer
34 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 ...
0
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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 ...
4
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3answers
1k views

What algorithm does ward.D in hclust() implement if it is not Ward's criteria?

The one used by option "ward.D" (equivalent to the only Ward option "ward" in R versions <= 3.0.3) does not implement Ward's (1963) clustering criterion, whereas option "ward.D2" implements ...
1
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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 ...
0
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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 ...
5
votes
3answers
93 views

Can a 1-D risk score (binary outcome) be sensibly used to create multiple treatment groups?

This question concerns predicted probabilities of a binary outcome, and the (I believe) misguided practice of making multiple cutpoints along a one-dimensional risk continuum -- cutpoints that create ...
4
votes
1answer
254 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 ...
27
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6answers
11k views

Time series 'clustering' in R

I have a set of time series data. Each series covers the same period, although the actual dates in each time series may not all 'line up' exactly. That is to say, if the Time series were to be read ...
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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 ...
1
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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 ...
2
votes
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 ...
4
votes
3answers
60 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
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. ...
2
votes
2answers
127 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 ...
0
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1answer
21 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 ...
6
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1answer
224 views

k-means|| a.k.a. Scalable K-Means++

Bahman Bahmani et al. introduced k-means||, which is a faster version of k-means++. This algorithm is taken from page 4 of their paper, Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & ...
2
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2answers
379 views

Do I need to drop variables that are collinear before running kmeans?

I am running kmeans to identify clusters of customers. I have approximately 100 variables to identify clusters. Each of these variables represent the % of spend by a customer on a category. So, if I ...
-1
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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 ...
0
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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 ...
1
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1answer
222 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. ...
0
votes
1answer
48 views

Is this a case of semi-supervised classification?

This is a question about proper terminology related with what is understood with "Semi-Supervised Classification". This is my context: I have a rule-based classifier. I know for sure I can classify ...
1
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2answers
435 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 ...
1
vote
1answer
113 views

Similarity between different length vectors containing related items

I have a vector (V1) with which I need to calculate the similarity of other vectors (ex V2,V3 ... ) which may be of different lengths. The different angle here is that the elements inside the vectors ...
1
vote
1answer
23 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
vote
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 ...
4
votes
1answer
257 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 ...
1
vote
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 ...
-1
votes
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 ...
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
3answers
110 views

Analysing data on importance ratings

I had following question in my questionnaire: Rate the following factors: price, quality, advertisement, brand, reference from 1 (very important) to 5 (least important) that may have influenced your ...