k-means is a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.

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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 ...
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14 views

I understand the algorithm for k means.But don't understand on how to apply it on testing data

I am having problems not on understanding the k means algorithm but on how to apply it on training ,validation and testing data.Is it like this: Training phase: Apply k-means on the input data and ...
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1answer
17 views

Proof that points change clusters less often as iterations proceed in k means

Is there a way that to prove the following: In k-means clustering, as the iterations proceed, the data points tend to stay in their existing clusters, overall, because the replacement of the centroid ...
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50 views

Plotting k-means clustering with mixed numerical/categorical data

I have a dataset in CSV format that looks as follows: ...
2
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44 views

Clustering a correlation matrix

I have a correlation matrix which states how every item is correlated to the other item. Hence for a N items, I already have a N*N correlation matrix. Using this correlation matrix how do I cluster ...
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1answer
60 views

LDA, PCA and k-means: how are they related?

I am trying to understand how linear discriminant analysis (LDA) is related to principal component analysis (PCA) and k-means clustering method. As an example, here is a comparison between PCA and ...
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1answer
47 views

K-means initial centers membership

I'm trying to plot all the steps of a k-means algorithm with r, but I can't. The k-means algorithm works in this way: Step 1. Initialize the center of the clusters Step 2. Assign the closest ...
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2answers
71 views

Clustering based on large Jensen-Shannon Divergence distance matrix

I have a dataset with large number of features and about 15 000 observations. I’m using a probability distribution distance metric related to Jensen-Shannon divergence (JSD) to cluster the ...
5
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1answer
130 views

k-means|| a.k.a. Scalable K-Means++

Bahman Bahmani et al. introduced k-means||, which is a faster version of k-means++. This algorithm is taken from page 4 of their paper, Bahmani, B., Moseley, B., Vattani, A., Kumar, R., & ...
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92 views

K-means++ algorithm

I try to implement k-means++, but I'm not sure how it works. I have the following dataset: (7,1), (3,4), (1,5), (5,8), (1,3), (7,8), (8,2), (5,9), (8,0) From the wikipedia: Step 1: Choose one ...
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1answer
55 views
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24 views

Determining k in k-means clustering by community detection in graph

I am faced with a problem of choosing an appropriate number of clusters in highly dimensional data. I've read many approaches to determine the number of clusters, and finally came to a solution and I ...
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45 views

Outlier detection with data (which has categorical and numeric variables) with R

Scenario I have a project about fraud detection where i need to find outliers by kmeans. I have a dataset about bank credits length of 1000. There are 21 columns (14 categorical, 7 numeric ...
2
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1answer
58 views

K-means clustering feature selection

I have a set of English and foreign language documents that I would to perform k-means clustering on to find document groups by topic. These documents are concatenated social media comments for ...
2
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1answer
22 views

Can I use HCA-Ward's cluster-centers to run a K-means including a new item, to see to which cluster is more similar to?

Thank you for reading my question. I have an archaeological case-study, that we can call "Site1", that I want to compare with 9 others "Sites" studied by other scholars. For all of them I have 8 ...
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2answers
41 views

Which K-mean algorithm I have to use for this problem?

Perform a k-means Clustering (non-iterative algorithm) using k=2 randomly initialised centroids (cluster prototypes), and the Euclidean distance. At the moment I manage to understand you can use ...
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1answer
22 views

Comparing / quantifying clusters

I have 'n' observations which are classified in two classes: Class A and Class B. The observations are mis-balanced with Class A constituting around 90% of the samples and Class B around 10%. The ...
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2answers
6k 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 ...
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9 views

Bisecting K-mediods [duplicate]

Is there an algorithm like Bisecting K-mediods and what would its advantages/weaknesses be? It seems to me that it could be used well in combination of Dynamic Time Warping for clustering time ...
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29 views

K-means for dataset with 10 million observations and k = 40 using packages “weightedKmeans”

I am trying to employ K means algorithm on a dataset which has 10 million observations. The unique identifier is a 9 digit US Zipcode and data is collected by a bank on its customers (regarding their ...
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2answers
57 views

Bisecting K-means using Dynamic Time Warping

I'm trying to cluster time series of different length and I came up to an idea to use DTW as a similarity measure, which seems to be adequate, but the thing is, I cannot use it with K-means, since ...
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1answer
49 views

k-means triangle inequality

This page explains very good how k-means works: http://mnemstudio.org/clustering-k-means-example-1.htm Somewhere I heard that there is some algorithms, which use triangle inequality to speed up the ...
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1answer
17 views

K-Means Clustering on Distributed System

Can anyone explain how the k-means clustering algorithm converges on distributed systems? It seems that each node in our hadoop cluster would simply find a local optimum. How do we update across ...
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1answer
86 views

k-means vs k-means++

As far as I know k-means picks the initial centers randomly. Since they're based on pure luck, they can be selected really badly. The K-means++ algorithm tries to solve this problem, by spreading the ...
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1answer
29 views

classification of the groups [duplicate]

Let's say I have two variables: height and weight. I want to produce 4 groups: high height/high weight, high height/low weight, low height/ high weight and low height/low weight. I also want to see ...
0
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2answers
58 views

Use of bootstrap in clustering algorithms

Are there clustering algorithms that take advantage of bootstrap? For example can one combine bootstrap with a standard K-Means algorithm to scale K-Means. I was thinking if the following at a ...
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1answer
99 views

Clustering into teams of fixed size

There is a particular team-based video game that exposes a ladder of individual ratings for each player that looks like this (player, rating, wins, losses): A, 2000, 35, 12 B, 1900, 41, 19 C, 1800, ...
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8 views

Clusteriod questions

I would like to clear some things up because I'm confusing everything. A $clusteriod$ is a coordinate for the mean value of a cluster? So if I have a 2-d .csv file I wish to perform kmeans, the ...
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1answer
33 views

Cluster Sequences of data with different length

I need to cluster sequences of data that have different length. I am using Matlab and my first question is related to the method. Is KMeans sufficient to achieve this? IN KMeans I have to use the ...
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52 views

Initial clusters in Kmeans clustering using mahout

I am trying to perform kmeans algorithm on data using . The option that has to be passed while running need a path to initial clusters. Can anyone tell me how can we have initial clusters even before ...
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31 views

K-means clustering with one column

I'm new to the k-means clustering algorithm. I have the following data: 228,796 321,523 222,664 257,265 4,174 8,25 327,531 ... These are total electricity ...
3
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1answer
37 views

Can I use k-means with a distance matrix composed of percentages? [duplicate]

I have objects o1, o2,...,on and for each pair I calculate a value that measures the pair's difference. This is a percentage, so for example o1o2 differ by 56%. Now I want to cluster this data. I can ...
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0answers
24 views

“Hierarchical k-means” wrong sample assignment

I am working on a hierarchical k-means scheme which is translated into sequential k-means in my case. Let say I have 10k samples (objects to cluster) which I want ...
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58 views

Middle point between k-means and DBSCAN in R

I have a big data sample of unrelated events in lon,lat,date format (booking locations to dispatch). I am trying to divide these events into clusters (k=50) where I ...
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1answer
30 views

Unnatural clustering with known clusters shapes and optimization criteria

My question is similar to this question Clustering with shape prior, but with additional information. The second answer suggests a mixture model approach to this problem, which is something like ...
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1answer
62 views

What kernel function can be used to project data into a feature space that is a “circle”?

I am working with cyclical data (Days 1-7, hours 1-24). I want to project it into a feature space that can understand that 1 and 7 are close days and 1 and 24 are closer than 22 and 24, etc, and then ...
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14 views

Data set with negative silhouette

I want to construct data set 𝐷 in 2 dimension, with a clustering {𝐶1, 𝐶2} computed by k-means with the property: which full fills the condition: There exists a point $𝑜∈𝐷$ with a negative ...
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1answer
55 views

Result of K-Means Algorithm Not Desired

I am learning about K-means algorithm, and I have generated a dataset with 150000 data points, with 10000 points per cluster. (Scatter plot at the bottom) When I run K-means on the dataset, I first ...
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1answer
34 views

Efficient weighted 1D Clustering (Grouping)

I'm dealing with the simple problem of grouping a set of 1-dimensional data (1 feature) according to its distribution in the 1-D space. I know exactly the number of groups I will like to get. So for ...
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2answers
81 views

k-mean clustering of week-times

I have data of meeting times. The data has weekday and hour of the day. I want to cluster the meeting times (I have reason to believe there are two different kinds of meetings that tend to occur at ...
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1answer
69 views

Why is k-medians typically used with Manhattan rather than Euclidean distance?

K-medians is typically used with Manhattan distance rather than Euclidean distance. Why is this?
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1answer
56 views

K means clustering of variable with multiple values

I have a sample data below that is from a large data set, where each participant is given multiple condition for scoring. ...
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47 views

K-means and maximum likelihood!

Is there any relation between k-means and the maximum-likelihood estimate in unsupervised learning? Any references would be appreciates! Thank you!
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215 views

Clustering a long list of strings (words) into similarity groups

I have the following problem at hand: I have a very long list of words, possibly names, surnames, etc. I need to cluster this word list, such that similar words, for example words with similar edit ...
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112 views

How is Weka calculating nominal attributes for K-means clustering?

I have both numeric and nominal variables in my dataset. Before applying clustering in Weka, I specified nominal variables to Weka and select K-means clustering. It is good that my nominal data seems ...
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28 views

Selecting the right BIC value

I'm using the hddc for an assignment to find the optimal number of clusters. The dataset is 9-dimensional and consists of 200.000 rows, however, the BIC values that I'm getting are really high. How ...
0
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1answer
33 views

k-means for set of integers [duplicate]

I'm not really sure where to begin with this but for a simplified example I have a set of integers: {11, 22, 3, 12, 1, 23, 21, 13, 2} I need to partition these into K=3 clusters with initial ...
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1answer
783 views

K-means on cosine similarities vs. Euclidean distance (LSA)

I am using latent semantic analysis to represent a corpus of documents in lower dimensional space. I want to cluster these documents into two groups using k-means. Several years ago, I did this using ...
0
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1answer
2k views

Calculate P value for the correlation coefficient

I would like to understand how people add the P value on a figure for means (Y axis) by age, volume or any other variable (x axis). How did they calculate the P value here? Please check the following ...
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162 views

k-means cluster, How to re-calculate centroid when using cosine similarity?

I have a requirement using k-means cluster method with cosine similarity instead of Euclidean distance. for example: ...