Cluster analysis is the task of 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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What are features that distinguish clustering, blind signal separation and dimensionality reduction?

In terms of input -> [process] -> output what are features that distinguish clustering, blind signal separation and dimensionality reduction? From this ...
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526 views

Metric and Clustering Method

I need some suggestions regarding what kind of metric and clustering analysis I should use. I read a lot of posts but didn't get any hints about this type of data. I have a 3000*5000 matrix, where ...
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338 views

Clusters produced by R intersect

I am new here - and relatively new to statistics, data mining and R. I am trying to understand why my data is not clustering correctly - or if I am reading it wrong. Shortly about the project: My ...
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2answers
362 views

Organizing cluster analysis results in a database

I'm a newbie in cluster analysis so please excuse me if my question seems to be very basic. I'm using SPSS and Matlab for performing cluster analysis in a variety of datasets. Dendograms are great for ...
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285 views

Clustering algorithm and distance function for sets

I am willing to run a clustering algorithm on data records consisting in sets each one representing the features enabled at a certain time. Is there any clustering algorithm you would recommend me to ...
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3k views

Good clustering Java library

I'm looking for a good Java library implementing several clustering algorithms. I'll have to cluster some programs execution traces and I still don't know which algorithms I am going to need, so I'd ...
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630 views

Are Bayesian approches used for classification (supervised) or for clustering (unsupervised)?

Are Bayesian approaches (static and dynamic) used for classification (which is supervised) or for clustering (which is unsupervised)? or can they be used for both ? I even see that for instance to ...
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Singular covariance matrix in Mahalanobis distance in Matlab

I am using the Mahalanobis distance to classify an unknown 64-dimensional vector into one of 75 classes. There are n samples of 64-dimensional vectors for each class, arranged into an Nx64 matrix ...
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11k views

Supervised clustering or classification?

The second question is that I found in a discussion somewhere on the web talking about "supervised clustering", as far as I know, clustering is unsupervised, so what is exactly the meaning behind ...
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65 views

Clustering based on interactions between members of two groups

I have a large dataset from a survey that describes what web pages people use. So for each person I have a list of pages that they visit and how frequently they visit them. What methods can be used to ...
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3answers
483 views

Can unsupervised evaluation measures for clustering replace a supervised evaluation measure?

Is it possible to have the same evaluation performances when comparing some clustering algorithms using many unsupervised evaluation measures instead of a supervised one ?
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504 views

Evaluating clusters of first-order Markov chains

I clustered my dataset of several thousand first-order Markov chains into about 10 clusters. Is there some recommended way how I can evaluate these clusters and find out what the items in the ...
2
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2answers
639 views

Finding most informative feature subsets given dataset, clustering algorithm and gold standard partition

I have an $n \times m$ matrix of data $\mathbf{D}$ as well as a $k$-partition $P$ of $n$ indices each representing a row in $\mathbf{D}$. Assuming an arbitrary clustering algorithm $A$, I would like ...
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1answer
232 views

How to compute distances within & between clusters, if clusters contain sequences?

I applied number of methods of clustering, and I want to evaluate these different methods using Dunn index, in this method I have to calculate the distance among clusters and among points in clusters. ...
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1answer
127 views

Document image analysis and retrieval with online incremental clustering

Is there any interesting problem in the area of "Document Image Analysis and Retrieval" which by nature needs an online/incremental clustering process ? The problem may be in the context of "Logical ...
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1answer
144 views

Is there any way to know if my clusters are meaningful or meaningless? [duplicate]

Possible Duplicate: How to tell if data is “clustered” enough for clustering algorithms to produce meaningful results? I have used hierarchical clustering, e.g, Ward's method, ...
3
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1answer
288 views

Data reduction of 3D points

[This is my first post to CrossValidated, I hope I'm not off-topic] I have data consisting of ~10^6 points in 3D space. We want to try out some surface fitting algorithms that cannot handle this high ...
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210 views

Finding “weak ties” in network data

So I am working on a new project looking at formal and informal networks between businesses in the same industry. Namely, I am looking at joint ventures, fractional acquisitions, minority share ...
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2answers
592 views

(hierarchical) cluster analysis with non-standard distance

My question is triggered by a question that was asked on stackoverflow: http://stackoverflow.com/questions/12198115/using-different-metric-for-hclust-linkage. The thing is this: I can formulate an ...
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2answers
162 views

How to cluster multidimensional (parametric) distributions?

It seems to me that it is possible to cluster unidimensional distribution in the space of their parameters, for example, we can try to find similar normal distributions in the space of $(\mu,\sigma)$ ...
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Appropriate clustering techniques for temporal data?

I have temporal data of activity frequencies. I want to identify clusters in the data that indicate distinct periods of time with similar activity levels. Ideally I want to identify the clusters ...
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1answer
3k views

Euclidean Distance b/t unit vectors or cosine similarity where vectors are document vectors

I was reading Similarity Measures and suddenly my whole world was falling apart. I have implemented a search engine using clustering techniques. For clustering, I used k means which uses Euclidean ...
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3answers
2k views

How do I find similarities between two sets of data

I have a group of data with 12 different football players, and they are rated for 11 different skills (speed, skill, flair, etc). I am looking to pair up individuals based on similar footballers, and ...
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2answers
870 views

Specifying the number of clusters in nearest neighbor clustering

I've got a distance matrix between examples. I want to cluster them into m clusters with a nearest neighbor algorithm which works like this: ...
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129 views

Strategies for Recovering Missing Data

I'm working on the following missing data problem to learn more about stats, probability, and machine learning, but I'm not really making progress solving it: I have a group of unordered, non-unique ...
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1answer
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761 views

Alternative to Otsu for dividing data into two groups

I need to be able to automatically divide a dataset into two clusters. There are heuristic reasons to expect the data to have two clusters which would be visually clear if one were to plot the data ...
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2answers
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How to calculate the variance of vectors for clustering?

I'm interested in various methods of measuring dispersion of vectors mainly for use in cluster analysis. I can think of three methods: Find the mean vector (centroid), then calculate the variance ...
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How to 'intelligently' bin a collection of sorted data?

I am trying to intelligently bin a sorted collection. I have a collection of $n$ pieces of data. But I know that this data fits into $m$ unequally sized bins. I don't know how to intelligently choose ...
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1answer
669 views

How can I calculate genetic similarities between groups?

I know how to calculate genetic similarity between genotypes, but I do not know how to calculate genetic similarity between groups. From cluster analysis, I found seven groups. Now, I need to ...
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355 views

Clustering & Time Series

I have a multivariate dataset that changes over time. I have extracted (and normalised) some features and used k-means to generate clusters over the entire span of the dataset. Now I want to see ...
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95 views

How to measure clustering in time duration?

I wish to measure clustering in the duration between stock trading. For example, a trade occurs at 1:59:19 and the next trade follows at 1:59:23 - the inter-trade duration is 4 seconds. I have roughly ...
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1answer
728 views

Fuzzy C-means and its stages of clustering

when dealing with soft and hard clustering techniques such as K-means and fuzzy C-means I run into abit of difficulty on the steps that FCM takes to calculate the clusters. For instance in K-means ...
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1answer
536 views

Market / Customer Segmentation - Merging two different segmentations

I have a database where each observation is a person. They were questioned on their attitude towards the consumption of X category of product. I have being using K-means to segment this data. I have ...
6
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1answer
1k views

How to statistically test whether there is any nesting effect in the data?

I have a sample including all students within 52 schools which have been randomly assigned to be treatment or control (we have group randomized trial in which all students in each school is either ...
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2answers
1k views

Hybrid (K-means + Hierarchical ) clustering

I have a huge dataset (50,000 2000-dimensional sparse feature vectors). I want to cluster them in to k (unknown)clusters. As hierarchical clustering is very expensive in terms of time complexity ...
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Is there an analytical function that approximates number of clusters?

I have a dataset {x_i} which form 3-10 clusters. Is there any analytical function of {x_i} that I may use to estimate the number of clusters in the dataset? The fact that there might be 3-10 clusters ...
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2answers
158 views

Incremental hierarchical clustering

I have an online k-means algorithm following this scheme: ...
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1answer
759 views

On cophenetic correlation for dendrogram clustering

Consider the context of a dendrogram clustering. Let us call original dissimilarities the distances between the individuals. After constructing the dendrogram we define the cophenetic dissimilarity ...
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5k views

Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data

I want to perform K-means clustering on objects I have, but the objects aren't described as points in space, i.e. by objects x features dataset. However, I am able ...
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1answer
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Cluster clickstream data

I've recently entered the realm of machine learning and a project I am working on requires me to cluster users based on the order they visited webpages on a website. I have data in the form of: ...
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1answer
2k views

BIC or AIC to determine the optimal number of clusters in a scale-free graph?

I am currently trying to partition a scale-free ("big") graph (around 20k vertices, 500k edges) into appropriate sub-graphs. Having derived the Laplacian of the graph, I tried running an approach ...
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2answers
2k 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 ...
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169 views

Detecting Clusters of “similar” source codes

Assume I have 400 students (that's in a big university) that have to do a computer science project, and that they have to work alone (no group of students). An example of project could be let ...
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171 views

How do I cluster data where some observation can not be in the same cluster?

Say I have 12 (x,y) positions: ...
3
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3answers
2k views

How is approximately unbiased bootstrap better than a regular bootstrap with regards to hierarchical clustering?

I asked this question at BioStar but did not get a reply, so Im posting the question here. What is a simple explanation of what an approximately unbiased bootstrap is with regards to hierarchical ...
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3answers
11k views

Is there an R function that will compute the cosine dissimilarity matrix?

I would like to make a heatmap with row clustering based on cosine distances. I'm using R and heatmap.2() for making the figure. I can see that there's a ...
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4answers
4k views

Clustering as a means of splitting up data for logistic regression

I'm trying to predict the success or failure of students based on some features with a logistic regression model. To improve the performance of the model, I've already thought about splitting up the ...
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1answer
248 views

Multiplicity in sample size calculation for stratified estimation problem

We want to determine the public opinion about a recently administered intervention in a health care network. There's a brief questionnaire of 10 Y/N questions in which we'd like to estimate ...
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What is an appropriate classification method for analysis of down-hole geophysical data?

I am looking for a method that may be used to classify and 'cluster' some multi-dimensional data gathered from drill holes, to impute some (only partially measured) physical properties of the rocks ...