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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Hierarchical clustering with mixed type data - what distance/similarity to use?

In my dataset we have both continuous and naturally discrete variables. I want to know whether we can do hierarchical clustering using both type of variables. And if yes, what distance measure is ...
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4k views

Comparing hierarchical clustering dendrograms obtained by different distances & methods

[The initial title "Measurement of similarity for hierarchical clustering trees" was later changed by @ttnphns to better reflect the topic] I am performing a number of hierarchical cluster analyses ...
14
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6answers
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Why does k-means clustering algorithm use only Euclidean distance metric?

Is there a specific purpose in terms of efficiency or functionality why the k-means algorithm does not use for example cosine (dis)similarity as a distance metric, but can only use the Euclidean norm? ...
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How can I group numerical data into naturally forming “brackets”? (e.g. income)

The following describes what I'm trying to accomplish, but it's possible an alternative problem statement can describe my goal: I want to divide the following numbers into groups where the ...
10
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k-means implementation with custom distance matrix in input

Can anyone point me out a k-means implementation (it would be better if in matlab) that can take the distance matrix in input? The standard matlab implementation needs the observation matrix in input ...
194
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2answers
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How to understand the drawbacks of K-means

K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just ...
48
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6answers
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How to tell if data is “clustered” enough for clustering algorithms to produce meaningful results?

How would you know if your (high dimensional) data exhibits enough clustering so that results from kmeans or other clustering algorithm is actually meaningful? For k-means algorithm in particular, ...
11
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2answers
6k views

Dynamic Time Warping Clustering

What would be the approach to use Dynamic Time Warping to perform clustering of time series? I have read about DTW as a way to find similarity between two time series, while they could be shifted in ...
125
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7answers
16k views

Why is Euclidean distance not a good metric in high dimensions?

I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high ...
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10answers
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How to decide on the correct number of clusters?

We find the cluster centers and assign points into different bins in k-means clustering which is a very simple algorithm and is found almost in every machine learning material on the net. But the ...
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5answers
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Where to cut a dendrogram?

Hierarchical clustering can be represented by a dendrogram. Cutting a dendrogram at a certain level gives a set of clusters. Cutting at another level gives another set of clusters. How would you pick ...
10
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Are there any non-distance based clustering algorithms?

It seems that for K-means and other related algorithms, clustering is based off calculating distance between points. Is there one that works without it?
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Comparing clusterings: Rand Index vs Variation of Information

I was wondering if anybody had any insight or intuition behind the difference between the Variation of Information and the Rand Index for comparing clusterings. I have read the paper "Comparing ...
49
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4answers
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Choosing clustering method

When using cluster analysis on a data set to group similar cases, one needs to choose among a large number of clustering methods and measures of distance. Sometimes, one choice might influence the ...
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6answers
18k views

Clustering with a distance matrix

I have a (symmetric) matrix M that represents the distance between each pair of nodes. For example, A B C D E F G H I J K L A 0 20 ...
32
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3answers
9k views

Is it possible to do time-series clustering based on curve shape?

I have sales data for a series of outlets, and want to categorise them based on the shape of their curves over time. The data looks roughly like this (but obviously isn't random, and has some missing ...
2
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1answer
7k 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 ...
30
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6answers
15k views

Time series 'clustering' in R

I have a set of time series data. Each series covers the same period, although the actual dates in each time series may not all 'line up' exactly. That is to say, if the Time series were to be read ...
16
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6answers
23k views

Clustering of mixed type data with R

I wonder whether it is possible to perform within R a clustering of data having mixed data variables. In other words I have a data set containing both numerical and categorical variables within and ...
12
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1answer
8k views

What is an acceptable value of the Calinski & Harabasz (CH) criterion?

I have done a data analysis trying to cluster longitudinal data using R and the kml package. My data contains of around 400 individual trajectories (as it is called in the paper). You can see my ...
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3answers
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What stop-criteria for agglomerative hierarchical clustering are used in practice?

I have found extensive literature proposing all sorts of criteria (e.g. Glenn et al. 1985(pdf) and Jung et al. 2002(pdf)). However, most of these are not that easy to implement (at least from my ...
6
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3answers
861 views

Using k-means with other metrics

So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
5
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2answers
4k views

Compute BIC clustering criterion (to validate clusters after K-means)

I'm wondering if there is a good way to calculate the clustering criterion based on BIC formula, for a k-means output in R? I'm a bit confused as to how to calculate that BIC so that I can compare it ...
5
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2answers
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Precision and recall for clustering?

Im confused about calculating precision and recall for clustering mentioned in this paper, Model-based Overlapping Clustering, A Banerjee et al., (last paragraph of column 1 on page 5). Suppose, if ...
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3answers
191 views

How to select or validate the selection of a clustering method?

One of the biggest issue with cluster analysis is that we may happen to have to derive different conclusion when base on different clustering methods used (including different linkage methods in ...
32
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7answers
13k views

How to do community detection in a weighted social network/graph?

I'm wondering if someone could suggest what are good starting points when it comes to performing community detection/graph partitioning/clustering on a graph that has weighted, undirected edges. The ...
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4answers
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How to interpret mean of Silhouette plot?

Im trying to use silhouette plot to determine the number of cluster in my dataset. Given the dataset Train , i used the following matlab code ...
17
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1answer
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How to define number of clusters in K-means clustering?

Is there any way to determine the optimal cluster number or should I just try different values and check the error rates to decide on the best value?
8
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1answer
3k views

Time series clustering

I have many time series in this format 1 column in which I have date (d/m/yr) format and many columns that represent different time series like here: ...
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3answers
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Evaluation measure of clustering (without having truth labels)

I'm clustering a set of data but I don't have truth document that allow me to evaluate the result of clustering (I have unlabelled data), so I can not use an external evaluation measure. In this case, ...
5
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1answer
8k views

Elbow criteria to determine number of cluster

It is mentioned in wiki page that one of the method to determine the optimal number of cluster in the dataset is "elbow method". Here the percentage of variance is calculated as the ratio of the ...
5
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2answers
5k views

Two-stage clustering in R

Is it possible to do 2-stage cluster analysis in R? Can anybody provide me resource on it?
9
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1answer
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Clustering: Should I use the Jensen-Shannon Divergence or its square?

I am clustering probability distributions using the Affinity Propagation algorithm, and I plan to use Jensen-Shannon Divergence as my distance metric. Is it correct to use JSD itself as the distance, ...
4
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2answers
2k views

Will the silhouette formula change depending on the distance metric?

I am using Silhouette width to compute the best value for k in k-means. As I am performing document clustering, I am calculating the values of a and ...
2
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2answers
2k views

How to use both binary and continuous variables together in clustering?

I need to use binary variables (values 0 & 1) in k-means. But k-means only works with continuous variables. I know some people still use these binary variables in k-means ignoring the fact that ...
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2answers
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Determine different clusters of 1d data from database

I have a database table of data transfers between different nodes. This is a huge database (with nearly 40 million transfers). One of the attributes is the number of bytes (nbytes) transfers which ...
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1answer
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What are the use cases related to cluster analysis of different distance metrics?

I'm trying to use different distance metrics like Euclidean, Manhattan, cosine, chebyshev among other distance metrics in my k-means algorithm to calculate distances between the data points and the ...
4
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2answers
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Approach and example of graph clustering in “R”

I am looking to group/merge nodes in a graph using graph clustering in 'r'. Here is a stunningly toy variation of my problem. There are two "clusters" There is a "bridge" connecting the clusters ...
6
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2answers
483 views

Can sub-optimality of various hierarchical clustering methods be assessed or ranked?

Classic agglomerative hierarchical clustering methods are based on a greedy algorithm. This means that they (many of them) are prone to give sub-optimal solutions instead of the global optimum result, ...
5
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3answers
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Dimensionality reduction using self-organizing map

Self-organizing maps are claimed to be an approach for dimensionality reduction. However, I am kind of confused about this claim. Consider the following example, I have a data set with 200 data ...
0
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1answer
293 views

Fit mixture of distributions to your time-series data in R

I have time-series data containing 1440 observations and the plot of the data is I want to fit the Gaussian Mixture Models (GMM) to the above plot, and for the same I am using Mclust function of ...
2
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2answers
64 views

Cluster analysis as a preliminary analysis

I want to produce four groups (high/high, high/low, low/high and low/low) using two continues variables and compare these groups in terms of a few dependent variables. I know that cluster analysis ...
21
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5answers
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Clustering a dataset with both discrete and continuous variables

I have a dataset X which has 10 dimensions, 4 of which are discrete values. In fact, those 4 discrete variables are ordinal, i.e. a higher value implies a higher/better semantic. 2 of these discrete ...
16
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8answers
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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 ...
7
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2answers
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Time series and anomaly detection

I would like to setup up an algorithm for detecting an anomaly in time series, and I plan to use clustering for that. Why should I use a distance matrix for clustering and not the raw time series ...
9
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2answers
8k views

Clustering a binary matrix

I have a semi-small matrix of binary features of dimension 250k x 100. Each row is a user and the columns are binary "tags" of some user behavior e.g. "likes_cats". ...
8
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3answers
2k views

Clustering distributions

I have several distributions (10 distributions in the figure below). In fact these are histograms: there are 70 values on the x-axis which are the sizes of some particles in a solution and for each ...
14
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2answers
3k views

Why does gap statistic for k-means suggest one cluster, even though there are obviously two of them?

I am using K-means to cluster my data and was looking for a way to suggest an "optimal" cluster number. Gap statistics seems to be a common way to find a good cluster number. For some reason it ...
13
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3answers
487 views

$L_1$ or $L_.5$ metrics for clustering?

Does anyone use the $L_1$ or $L_.5$ metrics for clustering, rather than $L_2$ ? Aggarwal et al., On the surprising behavior of distance metrics in high dimensional space said (in 2001) that $L_1$ ...
9
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1answer
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If k-means clustering is a form of Gaussian mixture modeling, can it be used when the data are not normal?

I'm reading Bishop on EM algorithm for GMM and the relationship between GMM and k-means. In this book it says that k-means is a hard assign version of GMM. I'm wondering does that imply that if the ...