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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C-Index for cluster analysis in Stata

I'm wondering how to calculate the C-Index for determining a 'good' number of groups in a cluster analysis in Stata? It's mentioned in this post (What is an acceptable value of the Calinski & ...
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19 views

Choose canonical values from clusters of erroneous ones

I suspect this is a statistical problem, but it may be just an algorithmic one. So, more formally: Given: a set C of unknown ‘canonical' values an error window e a set V of known values, s.t. ...
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176 views

What is the intuition behind the variation of information (VI) metric against others for cluster validation?

For non-statisticians like me, it is very difficult to capture the idea of VI metric (variation of information) even after reading the relevant paper by Marina ...
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2answers
158 views

Clustering with K-Means and EM: how are they related?

I have studied algorithms for clustering data (unsupervised learning): EM, and k-means. I keep reading the following : k-means is a variant of EM, with the assumptions that clusters are ...
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36 views

Analyze which items contributed the most to a significant anova simple effect

I have a generic question that I am not even sure how to formulate, but: Imagine that two categorical factors, A and B, that are well-known to interact. A is manipulated as a between-subject factor ...
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52 views

Exclude an RGB color from a set

This question has originally been posted on the math stack exchange, but these guys redirected me to you. Find it at http://math.stackexchange.com/questions/566065/exclude-an-rgb-color-from-a-set -- ...
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142 views

How to figure out what numbers often appear together in a dataset?

I have a lottery style dataset we produce internally (example below). I am trying to figure out which numbers appear most frequently together. Example questions: What are the top 10 pair of numbers ...
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1answer
78 views

Subspace clustering in R using package orclus

Currently I am working on some subspace clustering issues. I found one useful package in R called orclus, which implemented one subspace clustering algorithm called orclus. As stated in the package ...
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91 views

Cluster analysis and chi square test

I performed a cluster analysis and now want to consider if the variable “I have a smart phone” (yes/no) significantly differ between the cluster solutions by using chi square test. The p-value was ...
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2answers
208 views

How to determine which method is the most reasonable clustering results?

Method 1: Cluster by K-means with initial centroid {27, 67.5} Method 2: Cluster by K-means with initial centroid {22.5, 60} Method3: Agglomerative Clustering How can I know which method ...
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1answer
49 views

Are growth mixture models just Gaussian mixtures applied to coefficients of polynomials fitted to time-series data?

Am I understanding correctly that growth mixture model is just Gaussian mixtures applied to coefficients of polynomials fitted to the time-series data? For example, we have 1000 cases, with 3 ...
2
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1answer
67 views

Best approach to classifying 3-point trajectories?

I have a sample of about 300 subjects who have been measured at 3 different times (morning, afternoon, evening). The variable of interest can be assumed to be approximately normal. It appears that ...
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29 views

Ranking topics in K-Means

I understand that clustering is meant to group items together, is there any ways that we can quantify saying Cluster A is more important than Cluster B? Other than counting the number of items in a ...
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36 views

self organizig map with spectral clustering

I'm looking for implementation of self organizing map which for the clustering part uses spectral clustering rather than k-means. Does anyone knows something about it ?
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83 views

Post-hoc test in the context of cluster analysis

I performed a cluster analysis based on a principal components. Total sample size is around 140. Now I want to describe the differences among the clusters (by means of the components) while performing ...
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1answer
71 views

Community finding algorithms in large heterogeneous networks

Consider a network that consists of vertices with various meanings. For example: stack overflow users, keywords and user location when asking/answering a question. In this network, when a user asks a ...
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31 views

Measure topic centrality

Does anyone know any metric that measure topic centrality or topic stickiness (I've seen someone used this term too). For example Cluster 1 $d_1$ = { a, b, c }, $d_2$ = { b, c, d } and Cluster 2 ...
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1answer
159 views

Differences between clustering and segmentation

I have read about piecewise aggregate approximation (PAA) mining time series data, sliding window, top down and bottom up approaches for time series segmentation but these are applicable to single ...
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1answer
78 views

Measure of similarity/distance of data points in geographic space

Given two points $p_1=(x_1,y_1,t_1)$ and $p_2=(x_2,y_2,t_2)$, where $x$ and $y$ refer to the geographic coordinates in the plane, and $t$ to some measured value. Two distance measures to evaluate the ...
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34 views

Algorithms for keyphrase clustering

Are there any standard algorithms for keyphrase clustering. There are several algorithms for keyphrase extraction from a corpus. For e.g. this publication reviews some of the popular keyphrase ...
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73 views

Discriminant analysis for the validation of cluster analysis

I did a discriminant analysis for the validation of my cluster analysis. The cluster analysis is based on a PCA, so I used the components as the independant variables in the discriminant analysis. My ...
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112 views

Self organizing maps vs. kernel k-means

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel k-means to do the ...
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1answer
84 views

Should I standardize my variables for this particular case of cluster analysis?

I'm trying to cluster a list of records based on a (percentage) frequency distribution of variables which add up to 100%. Like Record1 - VarA(25%) VarB(25%) varC(50%) varD(0%) Record2- VarA(50%) ...
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33 views

Clustering microblogs

I have a microblog dataset with about 100k tweets and I would like to cluster them effectively using some less advanced algorithm. Is there anywhere I can find like a single-pass K-Means or similar ...
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1answer
111 views

Neighborhood analysis in Matlab using a dot plot

I have points in a 2D graph (coordinates: X,Y property: Z). I would like to find for every point the closest, for example, 5 points and save their properties. What would be the easiest approach? ...
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1answer
127 views

How to evaluate/validate clusters using multiple clustering methods

From some reading I did online, I understand that there are various methods for determining "similarity" used by different clustering algorithms. I am curious if it is good practice to run multiple ...
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30 views

Can I use the variance of a set of observation as heuristic to decide how many times repeat an experiment?

I am applying a clustering algorithm (K-means) to a huge set of high dimensional data points (SIFT descriptors). The algorithm is not deterministic and its results depend on the initialization of the ...
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2answers
117 views

Interpretation of the final cluster centers (cluster analysis)

I have a question concerning the interpretation of the final cluster centers. I performed a cluster analysis based on a pca (the variables are based on a five point Likert-scale). I got the following ...
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1answer
155 views

Simple way to categorize: terrible, poor, average, good, excellent

I have a data frame with the following: ...
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79 views

Use matrix feature for machine learning or cluster analysis

I have a bunch of features that I would like to use for classification/machine learning and cluster analysis. Normally I use single point values or transformations of values for features and ...
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1answer
292 views

Interpretation of PCA biplot?

I just ran my first ever PCA, so please excuse any naivety on my part. As input, I used five years worth of the following: S&P/ASX 200 A-REIT S&P/ASX 200 Consumer Discretionary S&P/ASX ...
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1answer
44 views

Weighing probabilities into a polygon

I have a collection of 4-member probability vectors (essentially proportions over 4 mutually exclusive categories). Is there a method to represent this data as a cloud of points inside a square? If ...
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19 views

Archetype path clustering

I have a sample of customers who took various paths to an end state. Between the start and end, they may have completed various interactions in between. Some of these interactions are dependent on ...
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2answers
102 views

Machine learning algorithms/approaches for class recommendations?

I am asking a theoretical question about machine learning in terms of clustering. Is it possible, given a set of data of classes that students have taken in a semester to recommend additional classes ...
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15 views

cluster - class dependency bag of words [duplicate]

I have three clusters which is obtained by clustering features from images. Lets assume I have two classes. Consider the following table, where c denotes the class number and k denotes the cluster ...
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108 views

Computing mutual information

I have a problem when computing the mutual information between two variables. Let's consider the following table: ...
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1answer
91 views

Sweeping across multiple classifiers and choosing the best?

I'm using Weka to perform classification, clustering, and some regression on a few large data sets. I'm currently trying out all the classifiers (decision tree, SVM, naive bayes, etc.). Is there an ...
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1answer
87 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. ...
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90 views

Sub sampling a dataset without losing the distribution

Suppose that I have a huge data set with high number of dimensions (components). For computational purposes I would like to take just a sample of this data set and work with it instead of working with ...
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2answers
41 views

Forgiving measure for external cluster validation

I'm using external validation to measure the success of a clustering algorithm. I don't consider my categories to be definite, so I'm looking for a measure that is forgiving to the following extent: ...
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1answer
54 views

Comparing two sets of clusters

I have some data which I wish to cluster. I would like to see how these clusters compare to the categories that have already been assigned. Is there some kind of metric or visualisation that will tell ...
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561 views

PyMC for nonparametric clustering: Dirichlet process to estimate Gaussian mixture's parameters fails to cluster

Problem setup One of the first toy problems I wanted to apply PyMC to is nonparametric clustering: given some data, model it as a Gaussian mixture, and learn the number of clusters and each cluster's ...
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1answer
52 views

Cluster “by part” instead of “as a whole”?

A definition before I start: A trajectory $t$ of length $n$ is here defined as a series of 2D coordinates $$\{(x_1,y_1), (x_2, y_2),..., (x_n, y_n)\}$$ Now I have a set comprised of such ...
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443 views

Calculate BIC to determine the optimal number of clusters (k-means clustering)

I have a set of data and want to know whether they fall in 1, 2 or 3 groups. I started exploring the question by using k-means in MATLAB. By just looking at the distance from the centroid of each ...
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16 views

How would you merge different hierarchies over the same domain?

Say that you have $K$ different tree hierarchies. Each tree $k_i$ has associated a subset of elements of a given domain, $S_{k_i} \in D$ and finally on the leafs of each tree you have subsets of ...
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1answer
87 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 ...
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38 views

How to correctly standardize spatial data?

I have measurements of a set of socio-economic variables on italian municipalities; the aim is to run a series of clustering algorithms on them, in order to see if any significant pattern of local ...
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1answer
117 views

Simple technique to identify number of clusters in dataset

I have a survey app (programmed using Ruby On Rails), and I am required to cluster the responses. I am using a Ruby library called AI4R and my code (in the event it is useful...) looks like the below ...
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1answer
159 views

distance measure of angles between two vectors, taking magnitude into account

Suppose I have two vectors, v1 and v2, from which I can calculate the angle between these two vectors as a measure of their ...
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1answer
250 views

Cluster analysis on ordinal data

I'd like to perform a cluster analysis on ordinal data (Likert scale) by using SPSS. I have around 140 observations and 20 variables that are scaled from 1 to 5 (1: I strongly agree, 3: neutral, 5: I ...