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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Discovering dis-associations between periods of time-series

I'm interested in discovering some kind of dis-associations between the periods of a time series based on its data e.g. find some (unknown number of) periods where the data is not similar with the ...
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16 views

Two step cluster analysis and a binary matching coefficient

I want to commence a two-step cluster analysis, since the database I am conducting analysis on contains important metric as well as nominal values. => Question #1: Should the binary and the metric ...
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14 views

Determining number of clusters K-means

I would like to automatically determine the number of clusters for K-means. I have read that elbow method could be used for that. The thing that confuses me is - I have to rerun algorithm while ...
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1answer
16 views

Clustering objects based on event timestamps

I have data for $n \approx 500$ objects, and for each observation I have between ~50 and ~200 observations. Each observation consists primarily of a timestamp when an event happened (and includes some ...
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9 views

Mean of vectors minimizes L2-norm [duplicate]

Recently introduced to data mining and am trying to understand how the mean of vectors minimizes L2-Norm/Euclidean distance. I've tried googling it and can't seem to find a proof as to how something ...
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13 views

Keyword clustering and frequency of keyword

Can anyone tell me Is there any relationship between membership of keyword and frequency of keyword ??? ...Both parameter (Membership and frequency) represent same thing about a KEYWORD or both are ...
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9 views

R Clustering Evaluation (Adaptive Kmeans)

i know there are several threads about this topic, but most i read, most i get confused. I'm doing a project that consists in clustering some data (news articles). I used adaptive Kmeans ...
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16 views

Two-step cluster analysis and autoclustering statistics

In older versions of SPSS there was displayed an auto-clustering statistics table with e.g. BIC criterion and its change for various cluster solutions. In later versions of SPSS (e.g. 21) I do not see ...
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1answer
33 views

How to compute the centroid of a cluster for Gower distances

I'd like to assess how scattered a cluster of binary vectors $X_j$ is, and as I understand the conventional way for doing this is: $$ S = \frac{1}{T} \sum_{j}^{T}\|X_j-A_j\|_p, $$ where $A_j$ is the ...
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24 views

What is the next step after acquiring the parameters(means, covar, priors) from GMM via EM

I am comparing the results achieved from clustering via K-means and GMM. For comparison I have accumulated a dataset of images. The training set consists of 359 images. I used SIFT to extract the ...
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1answer
45 views

Semi-supervised clustering high-dimensional data

I have a data set with 20% labelled samples and 80% unlabelled samples. I have $C$ classes. More than $C$ classes may exist in the data. Each sample is a $70$-dim vector. The size of the dataset is N. ...
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1answer
48 views

Hierarchical or Two-step cluster analysis for binary data?

(This question is an edited version of a question I previously posted which one user recommended would benefit from more focus). I have 2000 questionnaires from respondents which ask 33 different ...
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17 views

Cluster analysis of binary data [duplicate]

I have 2000 questionnaires from respondents which ask 33 different questions about which issues are present in their lives - i.e. alcohol abuse, domestic violence, mental health, child abuse, learning ...
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1answer
18 views

Affinity propagation comments

I was looking into affinity propagation for my similarity matrix problem and thought it would fit well. However, browsing literature I found this comment that basically breaks both legs of affinity ...
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1answer
35 views

recommendations for test data set(s) known to have well separated clusters [closed]

I am working on visualizing clusters in high dimensional space. I have had good luck with "real" data sets contributed to the UCI Machine Learning repository. Unfortunately, none of these data sets ...
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0answers
14 views

Difference between Weighted Average Entropy and Adjusted Mutual Information (for evaluating Clustering)

I was advised by my team leader to use this weighted average entropy to evaluating the performance of my dbscan clustering algorithm, and its mathematical formulation is: Scikit provides what many ...
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2answers
37 views

Suitabiility of the confidence score generated by SVM as a proxy for membership function

SVMs can generate a confidence score which is basically like a probability for a particular data item to belong to the particular class. I want to use this probability as a proxy for the 'distance' of ...
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14 views

How to split a class which is not very cohesive?

Using the silhouette width metric I can find out as to how well each object lies within its class after classification is done. I next find the average silhouette width of objects within a class and ...
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27 views

Average within-cluster distance using divisive clustering

I have to prove that the average within-cluster distance for 10 data points cannot increase when going from 1 cluster to 2 clusters (divisive clustering). Intuitively, it seems obvious that this is ...
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2answers
50 views

Understanding differences between large and small dimensional data when implementing algorithms

I'm working on a local outlier factor implementation based on the wikipedia entry : http://en.wikipedia.org/wiki/Local_outlier_factor This article seems to explain it in just two dimensional data. ...
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1answer
43 views

Analysing ranked data

I had following question in my questionnaire: Rank following factors: price, quality, advertisement, brand, reference from 1 (very important) to 5 (least important) that influenced on your buying ...
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1answer
18 views

which distance should be used with UPGMA clustering

I am trying to cluster a biological population on the basis of morphological characters using UPGMA clustering method, but I am not sure which distance should I use- Mahalanobis or Euclidean. What are ...
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0answers
20 views

How can you cluster a set of functions with unknown functional forms?

Say you've $N$ functions $f_N(x)$ defined on a regular grid $x$. You don't know the form of $f(x)$, you've only got several realizations of it. The different functions are related to each other ...
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15 views

Clustering with Restricted Boltzmann Machine

I am working with the basic RBM that can be found on Geoffrey Hinton's webseite and the MNIST dataset. What I want to do is graphically cluster the input data. I am working with a three layer network ...
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1answer
14 views

Cluster analysis for multi-response question

Let's say I have check-all-that-apply survey question. What kind of analysis can I run to understand if there are meaningful clusters (i.e. there's a cluster of people who choose A, B, C, and another ...
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13 views

Grouping search queries by similarity of search results

we run some studies using google search queries. We need to cluster these queries in topics and we would like to find some unsupervised approach. for each query we have the search results. I ...
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30 views

find group of rows in a matrix with pattern similar to another matrix

I've been scratching my head about this problem for some time: I have a big gene expression dataset (20k genes x 200 samples) in matrix A and i have a subset of this dataset (i.e. 40 genes x 200 ...
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1answer
24 views

How can I calculate cosine distance with multiple feature vectors and weigh them?

I have a dataset of text documents and I'm calculating pairwise cosine distances among them. For each document I have a bag of words vector, a vector built from entities extracted from the document, ...
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2answers
53 views

What are Clustering techniques for this case? [duplicate]

What type of clustering methods are available for ordinal, nominal and ratio variables? Suppose I have one ordinal, one nominal and one ratio variable; is there a common clustering technique that can ...
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14 views

Dirichlet process mixture model in Python

My question is concerned with the practical issues of using this model. I've tried to use Dirichlet process mixture model from Scikit learn python package to find a number of clusters in my data (1D ...
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40 views

How to represent outliers for multi dimensional data (local outlier factor)

Below graph taken from http://en.wikipedia.org/wiki/Local_outlier_factor displays "LOF scores : LOF image : This is great for two dimensional data but what about data > than two dimensions. How ...
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1answer
50 views

How to evaluate a clustering/unsupervised learning problem with massive amounts of data, with labels only for a small fraction of points

I'm wondering if anybody can point me to work on the evaluation of unsupervised learning where there are a very large (say hundreds of millions) number of points and manual labelling can only ever be ...
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1answer
19 views

How can one evaluate Incremental Clustering Algorithms, in particular the goodness of the clusters formed?

I have been studying an incremental clustering algorithm for a large set of data that exhibit an inherent dynamic behavior (that is new data can get added over time and some older data may get deleted ...
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1answer
18 views

Generating even-sized clusters in scikit-learn [duplicate]

I'm attempting to generate approximately even-sized clusters of a PCA'd feature set in Scikit-learn, but I'm not having any luck. I'm only familiar with KMeans clustering, and with that algorithm the ...
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24 views

Statistical significance of cluster validity

Hi I m working on a unsupervised problem to partition my dataset. I have access to the class labels for this dataset. Now I am trying to use Jaccard coefficient to compute correlation between cluster ...
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0answers
27 views

Spatial cluster analysis

Let's say I have a structure like this : This is a spatial region with measurement of plant population in each site. Black and red represent two regions with different intensities.The question is ...
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1answer
26 views

Cluster migration visualization

I have asked a very similar question at the Latex forum here, but in order to address the part of my question where I ask if there is a better way of visualizing the data I have, I wanted to cross ...
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2answers
28 views

Kmeans cluster size change quite a bit on each run

I am running a kmeans on a sample size of 1000 data. The data is scaled (z). When I run kmeans(df, nstart=25, centers=5)- it runs and I can get the size of each cluster. The largest group has 620 in ...
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0answers
31 views

Heteroskedasticity in a Linear Mixed Model SAS PROC MIXED

Asked a version of this question before but realized it needed some clarification. I have a dataset with identical twin pairs and fraternal twin pairs. I want to examine the relationship between an ...
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17 views

Cluster analysis (proximities)

I have a question regarding clustering. I have a symmetric matrix of 50 specialties (50 X 50) where each cell represents the number of observations related to each combination of specialties. Some ...
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0answers
17 views

Kmeans plotting on discriminant components

When you plot a kmeans model (in R) with the plotcluster() function, it plots the clusters against the axis of the 1st and 2nd discriminant components (dc). In reading about these axis- some state ...
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0answers
28 views

How to do clustering using genetic algorithm?

I am studying how to use genetic algorithm (GA) in clustering analysis on R programming. What I understand now is that we have to determine fitness function in GA. That is, we have to minimize within ...
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0answers
38 views

Variable Clustering Analysis

I have a data set that consists of 143 variables (~11000 observations), and I wish to do variable clustering to reduce the dimension. I am using hclustvar function ...
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1answer
31 views

Autoclass in R/Python? [closed]

Are there any packages that implement the Autoclass/ Naive Bayes Clustering algorithm in R or Python? Alternatively, what are some other clustering algorithms that can handle both categorical and ...
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2answers
23 views

How to propositionalize a relational data set for clustering analysis?

I am working with a data set of students and their courses for a single semester, attempting to cluster based on the courses & various other attributes where "courses" are the "many" side of a ...
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18 views

Clustering using different distance measures [duplicate]

I do unsupervised clustering for a dataset using k-means algorithm. I want to know what is the difference between different distance measures (Euclidean, cityblock, cosine and correlation,...etc). I ...
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0answers
55 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
24 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 ...
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2answers
29 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 ...
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0answers
20 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 ...