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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54 views

Clustering structured data: Assessing the similarity of documents that appear in tree structure

Usually when performing text document clustering, similarities across documents are assessed based on the lexical content of documents. But, in my problem, I wish to consider both the lexical content ...
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1answer
39 views

Motivations for Shi-Malik Algorithm

So I've been trying to make sense of the clustering algorithm on page 6 of this paper. Are the "first" k eigenvalues they refer to the smallest eigenvalues? What are the $y_i$ exactly? I don't ...
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0answers
29 views

Methods for temporal patterns extraction

For example a video or series of images, or usage patterns data on a website, or a univariate time series, is there some flexible methods for extracting patterns of any length, such as head ...
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1answer
69 views

Clustering large movie dataset using k-medoids?

I have to cluster a movie dataset of 10000 movies. A movie has attributes like Genres, Actors, Directors, Year. Earlier I thought that we can use a simple clustering algorithm like k-medoids and the ...
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1answer
35 views

Cluster analysis

I am trying to cluster cells (1×1km) over a specific area. Each cell is composed of various habitats defined by a code. (Each habitat consists of 3 parameters, so a habitat code looks like e.g. ...
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1answer
80 views

Comparison of close data sets

I'm studying around 100 sets of temperature ($N_{sample}=500$), which depends $4$ explicative variables such as power or speed. The dependency is always the same in each set, but sometimes the mean ...
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1answer
53 views

Evaluation measures of overlapping clustering

I have a dataset of Facebook users and a set of different clustering algorithms. The project goal is to draw up a rank between these algorithms in order to understand which of them are the good ones. ...
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2answers
53 views

What clustering algorithm can be used with a distance matrix and without feaures?

I have a dataset of binary files. I can't do feature extraction on them. I just computed the distance between every pair of file in the dataset with a distance metric (NCD = Normalized Compression ...
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0answers
12 views

Determining number of clusters with maximum jump or minimum variance

I want to determine a number of clusters, but I don't know how to do it. I want to use kmeans and select a number of cluster with minimum variance or with a maximum jump in variance. I plot variance; ...
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1answer
86 views
4
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2answers
112 views

Is there an advantage to squaring dissimilarities when using Ward clustering?

Is there a reason to prefer squaring or not squaring the dissimilarities when clustering with Ward's method? The question is motivated by the following statement in the documentation for R's ...
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1answer
66 views

What are the most common metrics for comparing two clustering algorithms (especially density based clustering) [closed]

When it comes to compare a new clustering algorithm, one always wants to show the advantages of his/her method over existing and well known methods. Going this way may mislead one to ignore ...
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0answers
19 views

Subspace clustering with random transformation

One approach for clustering a high dimensional dataset is to use linear transformation, and the most common approaches are PCA and random projection (where random projection arises from the ...
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1answer
156 views

How to calculate purity?

In cluster analysis how do we calculate purity? What's the equation? I'm not looking for a code to do it for me. Let $\omega_k$ be cluster k, and $c_j$ be class j. So is purity practically ...
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2answers
39 views

Clustering hetrogeneous data types: ordinal, interval

Say I have data that I'd like to cluster that has different dimensions that are of different data types. For example: ordinal: You mood today: Very happy, happy, neutral, sad, very sad Ratio: Age: ...
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5answers
153 views

Kolmogorov-Smirnov test - reliability

Description I want to use Kolmogorov-Smirnov test to check how given clusters of 1D points differs from normal distribution (original question here: How to test which data match model at best). I am ...
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1answer
31 views

How to test which data match model at best

Description I have 1D data with $N$ normally distributed clusters. I have to find a cluster, which is the worst (differs at most from the normal distribution). My approach I calculate $sq = ...
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1answer
47 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 ...
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1answer
24 views

Algorithms that use multiscaler properties of data to cluster

I was thinking of devising a clustering algorithm (for fun and kicks) that would cluster data by looking at the distribution of the data at multiple scales. For example say my data was distributed on ...
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0answers
49 views

How should I interpret GAP statistic?

I used GAP statistic to estimate k clusters in R. However I'm not sure if I interpret it well. From the plot above I assume that I should use 3 clusters. From the second plot I should choose 6 ...
3
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1answer
112 views

Bayesian Networks and discretization of variables using K-means clustering

In many approaches to learning Bayesian Networks a solution to tackle continuous variables is to discretize them and apply one of the well established techniques for learning Bayesian Networks ...
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0answers
19 views

How to compare different distance measures in time-series clustering?

My aim is to cluster 126 time-series concerning 26 weeks (so each time-series has 26 observation) by partitioning around medoids. Before clustering I wanted to compare which distance measure is the ...
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1answer
34 views

How do multi-attribute edge-weights influence community detection?

My graph consists of a computer network topology where each vertex is a physical node/device (depicted using its IP address). Two vertices will have an edge if the nodes have had communication with ...
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0answers
16 views

Clustering groups that have replicated measures: hierarchical clustering on group-average VS regression tree

I measured 2 continous dependent variables (V1 and V2) on 10 occasions (10 replicates) for each of 4 groups. I aim to cluster my groups. i.e. I dont want to cluster replicates, since this could mix ...
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1answer
98 views

Hierarchical clustering of correlation matrix

I have a correlation matrix of 8,854 * 8,854 size. These are Pearson correlation coefficient values in the matrix. I want to perform Hierarchical clustering and create good resolution images like I ...
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1answer
38 views

Hierarchical clustering with NCD as the distance metric

I am trying to cluster a bunch of executable files, and I want to use NCD ( normalized compression distance) as the distance metric. Is there any software package which lets me do that? Update: I'm ...
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1answer
37 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
20 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
73 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
38 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
32 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
52 views

How do we analyse likelihood in a dataset? [closed]

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 ...
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3answers
67 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" ...
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1answer
52 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
79 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
19 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
47 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
50 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
60 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
58 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
29 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
39 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
134 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
104 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 ...
0
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1answer
55 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 ...
0
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1answer
34 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
76 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
81 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
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1answer
46 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
37 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 ...