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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10 views

Converting a set of tweets into vectors for clustering

I have a large set of tweets to which i plan to use cosine similarity to cluster the tweets. I found NLTK's GAAC to be good but how do i convert the tweets into vectors? In ...
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0answers
17 views

Sample size question - ill posed question?

I'm working on a project in which we are performing clustering on high dimensional data (~1000 variables) and looking for subpopulations of observations that result from clustering. Think gene ...
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3answers
48 views

Problem with PCA

I am trying to do some PC analysis on my data coming from lipids measurements in different samples. I only have one factor: if samples are diabetic or non-dibetic. Here is the PCA graph I get: As ...
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1answer
28 views

One huge cluster + small ones with vector-space model + cosine distance

I'm trying to cluster meaningfully a set of objects characterized by a vector space (bag-of-words) model. Each of those 5000 objects has 1-8 features ("words") from a set of 5500 possible. I used a ...
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1answer
26 views

Clustering using density fields

I like to tinker in my spare time with clustering algorithms. Over the past few days I was attempting to tinker with a clustering algorithm using density fields of the data. I tried several ...
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2answers
45 views

How do we analyse likelihood in a dataset? [on hold]

I am working to analyze poverty rate using census data. I have a huge dataset. I want to extract the likelihood from this dataset in order to create patterns for energy consumption. What is the ...
-3
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1answer
75 views

k-Means Clustering vs. Hierarchical Clustering [on hold]

Can you please help with finding a good comparison k-Means Clustering vs. Hierarchical Clustering? I.e. What are the advantages of each of them over the other one.
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3answers
40 views

Agglomerative Hierarchical Clustering “complete linkage” as opposed to “single linkage” dendrogram

Will any dataset clustered via each of the following methods: Agglomerative Hierarchical Clustering using "complete linkage" method Agglomerative Hierarchical Clustering using "single linkage" ...
-1
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1answer
39 views

Cluster with distance threshold in R

I'd like to get clusters with a maximum inner distance threshold. Now I use hc <- hclust(d) and cutree(hc, numofclasses). ...
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2answers
28 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 ...
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1answer
15 views

clustering of singular values

let us consider following graph of singular values i want to make some kind of clustering of these data,namely to seperate main components from non main components,let say signal components ...
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0answers
24 views

Feature Selection for look alike modeling using k-NN

I have a list of items and various parameters for each items. For each item on my list i need to identify items which are similar to the item from my whole population . I am planning on using K-NN ...
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0answers
37 views

How to compare two different clustering approaches?

I'm making a project connected with identifying the dynamics of sales. My database concerns 26 weeks (so equally in 26 time-series observations) after launching the product, 126 time-series=126 ...
2
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1answer
32 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 ...
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1answer
34 views

Performing hierarchical clustering on a large data set

I have been applying complete linkage on about 5,000 points using matlab with no problem. I want to extend this method to much more elements. It would take me a long time to process my data to test ...
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0answers
14 views

Complete Linkage Clustering of 3D data space coordinates

I have a large dataset of 3d points (XYZ coordinates) and I would like to perform hierarchical clustering using complete linkage method with Euclidean distance as clustering metric. Additionally, ...
2
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0answers
27 views

Which steps have to be done before fitting logistic curve to time-series?

I want to cluster time-series concerning sales of products. In the database I have 26weeks after launching each products and units sold each week. One of the method of clustering is to cluster ...
0
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1answer
59 views

usefulness of k-means clustering on high dimensional data [duplicate]

I wonder what is the usefulness of k-means clustering in high dimensional spaces, and why it can be better (or not) than other clustering methods when dealing with high dimensional spaces.
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0answers
54 views

Variance Inflation Factor to Address Spatial Grouping with Binary Dependent Variable

I want to obtain reliable standard errors of the estimated coefficients from a regression of y on x. The observation for each individual consisted of a value of the y variable, which is binary, and a ...
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1answer
44 views

Which clustering algorithm shall I use?

I need some help My project aims to develop algorithms for spatial temporal analysis of Flickr, Twitter and Foursquare databases to detect any kind of significant changes, named as “Event” in real ...
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1answer
20 views

Silhouette scores for different distance metrics

I clustered a data set using PAM with a euclidean distance metric and a pearson correlation distance metric. The average silhouette value of the correlation clusters is higher at most points than the ...
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2answers
36 views

Streaming k-means

I want to perform something like streaming/online/out-of-core kmeans clustering on large data. Here is simple idea: Break all data into N chunks. Read from disk 1st chunk and calculate centroids ...
1
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1answer
55 views

cluster plot: working and interpretation ?

Recently I have come across usage of cluster plot, which combines k-mean clustering along with PCA. The plot shows different clusters plotted using first two PCs. I have checked some of the threads ...
2
votes
1answer
37 views

How does the Bayes' theorem equation generalize all sorts of regression/classification models?

I have been reading “Pattern Recognition & Machine Learning” written by Christopher M. Bishop for some time, but I am still a beginner. I wish to get a bigger view that summarizes regression and ...
2
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0answers
30 views

Cluster analysis on related factors

I am analyzing a public data set of information security incident data and trying to find "clusters" of related factors. Specifically, each incident is analyzed using VERIS for the actor's variety ...
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1answer
21 views

Evaluation indexes hypothesis for clustering

I read on the cluster analysis page of wikipedia: For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex ...
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0answers
40 views

Clustering time series of measurements in R

I have a dataframe consisting time series of measurements taken every hour for 366 days or a year. Below is shown a sample of hourly measurements for the first two weeks. I want to cluster days with ...
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0answers
11 views

k-way MinMax spectral clustering

Is there a k-way MinMax Cut Spectral Clustering which can be easily implemented? In the spectral clustering tutorial I found only 2-way MinMax cut.
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0answers
21 views

Finding multivariate clusters with survey data (in R)

I'd like to conduct a multivariate cluster analysis on data from the American Community Survey's PUMS microsample (individual level records). I've only performed cluster analysis before when there are ...
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0answers
19 views

Comparison of cophenetic correlation coefficients on different data sets

I applied the same hierarchical clustering (weighted) on two data sets: The first is a 'raw' data set, on which I didn't do anything, and the second on the same data set after I filtered it by ...
1
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1answer
35 views

Alternative to spherical K-Means for clustering large high dimensional dataset

What are some alternatives to Spherical K-Means for clustering very large datasets of high dimension? I'm looking for something that will be fast even on large datasets, and preferably will not ...
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0answers
18 views

processing a sound file for analysis of different spoken languages

So i have sound files for 5 languages by 2 person, thus my input data has 10 sound files. Now i want to cluster them based on the languages (thus 5 clusters) and not based on the speaker/voice ( ...
1
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1answer
28 views

Algorithm for scoring co-varying traits

I am sure this has been done, but I can't find quite the right approach. EDIT: Trying to explain better. The rows of colored boxes below are columns of molecular sequence data -- positions in a ...
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0answers
30 views

statistical significance test on cluster analysis result

I've done a hierarchical clustering on a data-set composed by 33 subjects and 2 continuous variables (called V1 and V2), which produces 3 clusters. Now I'm wodering if it make any sense to perform a ...
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0answers
24 views

Converting a spearman correlation to a euclidian dissimilarity

I am applying ward hierarchical clustering on a data set for which I have pairwise similarities. Since hierarchical clustering need a dissimilarity matrix, I am trying to convert my similarity matrix ...
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0answers
19 views

clustering variables of mixed types in R

I need to analyse questionnaire survey data with mixed data types (nominal, ordinal, continuous). I want to cluster the variables. So far I only have dead ends. I know I can use daisy in the ...
2
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0answers
26 views

What is a good technique for grouping objects based on binary or dichotomous traits?

I have a set of objects each of which has a list of traits. Data on the traits is binary: an object has a trait or does not. The number of objects that I have is moderately greater than the number ...
2
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0answers
29 views

Principles to create natural data for a clustering algorithm?

I have come up with an algorithm to cluster geospatial data points. I have quite a few volunteers (100) collecting data for me, using my app on their smart phones to check in at places. However, I'm ...
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0answers
16 views

Clustering Techniques

I'm a little new to data mining and would definitely appreciate some tips. I'm using clustering algorithms looking for possible grouping in some variables described below. I've been using the Excel ...
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0answers
23 views

combining two categorical variables

I have one five point Likert scale variable (importance levels) for accessibility to a certain facility, and another three-level categorical variable (preferred distance). I want to combine these ...
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0answers
34 views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
2
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2answers
110 views

Special method for forecasting on time-series clusters in R?

I'm doing a project related to identifying sales dynamics. My database contains 26 weeks after launching the product (so 26 time-series observations equally spaced in time). I used two methods ...
0
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1answer
29 views

Clustering Variable

I have a set of 10 variables (v1..v10) which are continuous. I have another two control variables ctrl11 and ctrl12 which of course are categorical. ctrl11 can take any values from 1 to 50 and ctrl12 ...
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0answers
35 views

Understanding and Implementing a Dirichlet Process model

I am trying to implement and learn a Dirichlet Process to cluster my data (or as machine learning people speak, estimate the density). I read a lot of paper in the topic and sort of got the idea. ...
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0answers
17 views

What is data augmented by the additive inverse?

I am reading Biclustering of expression data (Cheng and Church, 2000) The paper is about the Cheng and Church biclustering algorithm and its main metric, the mean squared residue (MSR). It is said ...
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0answers
29 views

Converting a similarity to a dissimilarity [duplicate]

I am working on a clustering problem for which I have to manually choose the number of clusters. I have a visualization tool that helps me decide whether the clusters are good. In order to ...
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0answers
22 views

What is a shift bicluster?

I am reading A comparative analysis of biclustering algorithms for gene expression data (Eren, Kemal, et al. - 2013) When explaining the Cheng and Church method, it says that: MSR was shown to be ...
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0answers
21 views

updating clusters in NCut spectral clustering

Are there any methods to update the clusters formed in k-way NCut when new data points arrive or any change in the similarity matrix?? thanks
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0answers
13 views

Clustering with dependent features

I have $n$ observations which I want to cluster based on $p$ features. Those $p$ features are dependent to each other (or correlated). If I use Euclidean distance, the weights of all features would be ...
1
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0answers
30 views

Vector Quantization of heavy tailed distribution

I'm generating with Monte Carlo simulation some stock price $X$. Once I have the stock price sample, I want to cluster it with 100 points $\hat{X}$. My problem is that the error associate with my ...