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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8
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1answer
611 views

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 ...
7
votes
2answers
2k 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 ...
9
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2answers
3k views

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 ...
7
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1answer
2k 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 ...
6
votes
4answers
9k views

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 ...
36
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6answers
10k views

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, ...
6
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4answers
9k views

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 cosine similarity as a distance metric, but can only use the Euclidean norm?
19
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5answers
11k 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 ...
9
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3answers
3k views

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 ...
30
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3answers
6k 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 ...
34
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5answers
15k views

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 ...
22
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8answers
12k views

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 ...
28
votes
6answers
12k 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 ...
36
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3answers
10k views

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 ...
21
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3answers
5k views

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 ...
1
vote
1answer
2k 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 ...
91
votes
2answers
10k views

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 data set and a pre-specified number of clusters, k, then I just ...
90
votes
6answers
10k 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 ...
14
votes
8answers
4k views

Visualization software for clustering

I want to cluster ~22000 points. Many clustering algorithms work better with higher quality initial guesses. What tools exist that can give me a good idea of the rough shape of the data? I do want to ...
17
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1answer
8k views

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?
17
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3answers
13k views

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 ...
5
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1answer
5k 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 ...
8
votes
1answer
4k views

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, ...
5
votes
1answer
3k views

Two-stage clustering in R

Is it possible to do 2-stage cluster analysis in R? Can anybody provide me resource on it?
4
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2answers
1k 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 ...
6
votes
3answers
362 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
votes
1answer
609 views

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 ...
6
votes
2answers
359 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
votes
2answers
2k views

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 ...
2
votes
2answers
49 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 ...
24
votes
7answers
10k 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 ...
15
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7answers
4k views

How to perform k-means clustering with only a distance function, not euclidean points?

I want to perform k-means clustering on some objects I have, but the objects aren't described by "points". However, I am able to compute the distance between any two objects (it is based on a ...
8
votes
1answer
2k 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: ...
8
votes
4answers
448 views

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? Thanks!
11
votes
1answer
5k 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 ...
8
votes
3answers
1k 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 ...
13
votes
3answers
451 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 $L_1$ ...
7
votes
2answers
4k views

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 ...
9
votes
4answers
3k views

Clustering procedure where each cluster has an equal number of points?

I have some points $X=\{x_1,...,x_n\}$ in $R^p$, and I want to cluster the points so that: Each cluster contains an equal number of elements of $X$. (Assume that the number of clusters divides $n$.) ...
4
votes
3answers
990 views

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 ...
2
votes
2answers
1k views

How to determine the number of clusters when using correlation as the distance?

How does using 1 - correlation as the distance influence the determination of the number of clusters when doing kmeans? Is it still valid to use the classical indices (Dunn, Davies-Bouldin...)?
1
vote
0answers
232 views

Difference between BIRCH and Two-Step Clustering

I don't have SPSS. But judging from the documentation you can find on the internet, SPSS "Two-Step Clustering" seems to be closely related to BIRCH clustering: Zhang, T., Ramakrishnan, R., & ...
0
votes
1answer
189 views

Separate time series data into two trends

I have a time series data-set that contains two separate trends(one trends has relatively lower values/ the other has higher values).If you plot it in excel, it will be very clear to see those two ...
97
votes
8answers
13k views

Detecting a given face in a database of facial images

I'm working on a little project involving the faces of twitter users via their profile pictures. A problem I've encountered is that after I filter out all but the images that are clear portrait ...
32
votes
6answers
7k views

Euclidean distance is usually not good for sparse data?

I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
15
votes
4answers
4k views

Assumptions of cluster analysis

Apologies for the rudimentary question, I am new to this form of analysis and have a very limited understanding of the principles so far. I was just wondering if many of the parametric assumptions ...
6
votes
4answers
570 views

Working through a clustering problem

Say I've got a program that monitors a news feed and as I'm monitoring it I'd like to discover when a bunch of stories come out with a particular keyword in the title. Ideally I want to know when ...
5
votes
3answers
2k views

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, ...
3
votes
2answers
3k views

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 ...
7
votes
2answers
855 views

Getting started with biclustering

I have been doing some casual internet research on biclusters. (I have read the Wiki article several times.) So far, it seems as if there are few definitions or standard terminology. I was ...