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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2
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2answers
108 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
23 views

Event centric clustering on R (Sports news)

i'm about to start a project on R and my goal is cluster Soccer news by event. in other words, each cluster must contain all news about a specific event from multiple sources. SO, my questions are: ...
2
votes
1answer
67 views

How can I evaluate the performance of a system that generates word clusters?

The word2vec tool uses deep learning to compute vector representations of words. They've mentioned that - "The word vectors can be also used for deriving word classes from huge data sets. This is ...
1
vote
2answers
24 views

How to compare two clusterings generated by two clustering approaches

I am currently working on a modification of a clustering algorithm to suit my problem domain. I want to know which methods are available for me to compare the centroids generated from the two ...
0
votes
0answers
24 views

PCA scores and cluster analysis

I have obtained PCA scores (Anderson-Rubin) and thought to use them in cluster analysis, but have got confused how to make interpretations based on them or as what type of variable they should be ...
0
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1answer
21 views

How to include noise in clustering evaluation?

When evaluating clustering methods which do have a definition for noise points (like dbscan), how noise will affect evaluation? Consider a clean dataset like well known Iris dataset. There is no ...
2
votes
1answer
113 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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1answer
23 views

Testing the hypothesis on clustering

I have a number of samples. For each, there is a time course of multivariate data defined, with $t$ timepoints ($t < 50$) and $n$ variables ($n > 100$). We have noted that the time courses of a ...
0
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0answers
31 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 ...
8
votes
4answers
475 views

Are there cases where there is no optimal k in k-means?

This has been inside my mind for at least a few hours. I was trying to find an optimal k for the output from the k-means algorithm (with a cosine similarity metric) so I ended up plotting the ...
1
vote
3answers
953 views

Clustering with Weka

I have to analyse a data set with weka clustering, using 3 clustering algorithms and I need to provide a comparison between them about their performance and suitability. The comparison may include a ...
0
votes
1answer
132 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
0answers
14 views

Interpreting R results, are the data multivariate normal?

I ran "mvn" using the "mclust" package in R using the following codes: mvn("EEE", data[,18:22], prior = NULL, warn= NULL) I am having trouble figuring out how to ...
2
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5answers
5k views

Categorical variables clustering with R

I wonder whether it is possible to perform within R a clustering of mixed data variables. In other words I have a data set containing both numerical and categorical variables within and I'm finding ...
0
votes
1answer
32 views

R codes for variation of information criterion using “mclust”

I am developing model-based clustering. First, I developed model-based clustering in R using "mclust." Next, I wanted to take 75% of the sample, re-run model-based clustering and compare the ...
6
votes
1answer
239 views

Memory requirements of $k$-means clustering

Can anyone tell me the factors that affect the memory requirements of $k$-means clustering with a bit of explanation?
0
votes
0answers
38 views

Cluster Matching/comparison

Description I have several datasets(from different subjects) with the same type of data. For each dataset I cluster the data using affinity propagation. Clustering is based on similarity distance ...
0
votes
1answer
23 views

Comparing mclust() and k-means centroids

I have some code that looks for clusters in x,y data. To check the number of clusters I use, I want to get the BIC. This is not possible (easily) using kmeans(), ...
5
votes
1answer
82 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 ...
6
votes
1answer
814 views

Clustering probability distributions - methods & metrics?

I have some data points, each containing 5 vectors of agglomerated discrete results, each vector's results generated by a different distribution, (the specific kind of which I am not sure, my best ...
4
votes
1answer
106 views

Clustering data that has mixture of continuous and categorical variabes

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 ...
3
votes
1answer
129 views

Distance between two Gaussian mixtures to evaluate cluster solutions

I'm running a quick simulation to compare different clustering methods, and currently hit a snag trying to evaluate the cluster solutions. I know of various validation metrics (many found in ...
0
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0answers
15 views

Appropriate PCA/ EFA rotation method

Question about appropriate PCA/ EFA rotation method. While I see that oblique rotation methods (e.g. promax) are suggested for correlated data (and I have correlated data) I see that the majority of ...
1
vote
1answer
26 views

Binomial data and PCA and cluster analysis

I have obtained responses on around 48 items measuring employer attractiveness. The goal of my analysis is to cluster respondents according to these employer attractiveness dimensions. I plan to ...
2
votes
1answer
65 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 ...
2
votes
1answer
168 views

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 ...
0
votes
1answer
17 views

Correlation or clustering of continuous score and discrete variable states

I have an experiment that produces a decimal score representing quality, and a bunch (5-30) of variables that each take on one of a set of discrete states. - The states are not meaningfully ...
0
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0answers
5 views

Streaming k means clustering Mahout

First of all, excuse me if this is not a good place to ask this question. Can anyone explain to me how streaming kmeans algorithm implemented in Mahout works? And can it be used for anomaly ...
1
vote
1answer
107 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 ...
0
votes
1answer
22 views

How can I determine local minima from a Kernel Density Estimation? [duplicate]

I have a fairly large 1-dimensional array that I am trying to cluster. I came across several other questions on this site where the top answer is to use a Kernel Density Estimation and then locate ...
0
votes
0answers
12 views

Term to Describe Strongly Clustered Data

I have some data which are strongly gathered into more than one cluster. I am looking for a term to effectively describe this phenomenon: e.g., multi-clustered data, which however seems to me that we ...
0
votes
1answer
249 views

F-measure for document clustering evaluation - NaN

I'm developing the Java application for text document clustering, and I'm researching some evaluation methods. I implemented F-measure (http://en.wikipedia.org/wiki/F1_score), but I have a problem - ...
1
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2answers
155 views

Similarity between objects based on tags (binary features)

I have five millions of objects each of them having one or more tags. How do I compute statistically sound similarity score between each pair of the objects taking into account that: There are 100 ...
0
votes
1answer
95 views

Three-dimensional phylogenetic tree “anchored” in a scatter plot

I have done a simple clustering (protoclust) using error-containing data. To determine distances, I used a simple "pseudo-d" distance, in which the absolute value of the difference between two points ...
0
votes
1answer
25 views

Mixing probabilities in mixture models using EM

Assume we want to estimate the mixing probabilities ($\pi_{k}$) for each member distribution in the mixture model. We know that $\sum_{m}^{K}\pi_{m}=1$, so we can formulate the optimization problem ...
4
votes
2answers
67 views

Using k-means with other metrics

So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
5
votes
1answer
380 views

What are the use cases related to cluster analysis of different distance metrics?

I'm trying to use different distance metrics like Euclidean, Manhattan, cosine, chebyshev among other distance metrics in my k-means algorithm to calculate distances between the data points and the ...
2
votes
1answer
61 views

Cross-validation for Comparing Clustering Techniques

I'm working on comparing multiple clustering algorithms to each other using the adjusted Rand index for a given dataset. We have a gold standard that we'd like to compare the obtained clustering ...
0
votes
1answer
32 views

How to Calculate silhouette coefficient in SPSS for clustered data set?

I am having a pre clustered dataset with data and the action cluster identified for it using a custom clustering method. I am looking to calculate silhouette coefficient on this clustered dataset ...
0
votes
0answers
22 views

breakpoint analyses on multiple series: how to detect common points

I have 20 time series that span the same period (100 days each), from 4 species sampled at 5 different location. I made a loop to perform a breakpoint analysis on all of them, resulting in 0 to 3 ...
0
votes
2answers
46 views

What's a good way to mentally visualize n dimensions in a k means

I've been using k-means to do some clustering and one of the ideas I'm struggling with is the n dimensions aspect. If I were clustering housing prices vs sq. feet its just a simple 2d graph. That I ...
0
votes
1answer
21 views

Is my understanding of how to calculate the reachability distance in local outlier factor correct?

Reading lof implementation at : http://www.cse.ust.hk/~leichen/courses/msc-it5210/lectures/LOF_Example.pdf the local reachability distance is given as : I don't fully understand this equation as ...
0
votes
0answers
7 views

Clustering techniques when there are two distinct but numerically similar clusters among others

I'm trying to cluster some spatial statistics for some behavioral estimation. However, parts of the trajectory report exactly 0 speed, which implies some kind of resting state which is different from ...
0
votes
1answer
223 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) ...
2
votes
1answer
455 views

Cluster analysis on weighted survey data with continuous and categorical variables

I am trying to perform cluster analysis on survey data where each respondent has answered several questions, some of which have categorical answers ("blue" "pink" "green" etc) and some of which have ...
2
votes
1answer
56 views

Time Series Data Mining Library?

Can anyone recommend a library for time series data mining tasks other than predictive modeling and statistical analysis? There seem to be a number for these purposes (e.g., Gretl), but nothing for ...
1
vote
1answer
13 views

Why is it called Epsilon for DBSCAN?

The two parameters of DBSCAN are epsilon and minimum samples. Shouldn't epsilon be called like "Circle radius"? Why is it called epsilon?
0
votes
1answer
154 views

Outlier detection using clustering and dissimilarity matrix in R

I have some problems in finding the outliers using clustering. The data.frame is ~20000 observations and each row has mixed types of variables(numeric, nominal and binary). What I want to do is to ...
4
votes
2answers
749 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 ...
2
votes
1answer
139 views

Dirichlet process mixture model with Bayesian hierarchical clustering

I am doing Bayesian hierarchical clustering. From my understanding, there are three basic points for this algorithm. Use marginal likelihoods to decide which clusters to merge Asks what the ...