Questions tagged [k-means]

k-means is a method to partition data into clusters by finding a specified number of means, k, s.t. when data are assigned to clusters w/ the nearest mean, the w/i cluster sum of squares is minimized

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one old-exam question and challenging option, is there any description?

I ran into a 2019-Entrance Exam question as follows: Answer mentioned is (4). but some search on google show me maybe (1) and (2) is equals to (4). any expert can mentioned idea about why (4) is the ...
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K-means++ for weighted clustering

I have implemented k-means for weighted points; that is, the final clusters take into account the fact that each input point is weighted. I wanted to initialize the clusters using k-means++, and I was ...
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K-means and data set

I'm looking for data sets which might allow me to show differences between K-means, K-means++ and K-means|| (scalable K-means Bahmani et al. ’12) which I've implemented. I need different datas set (...
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what's wrong with K-means++ [closed]

What's wrong with K-means++ ? That implies that we need K-means|| (scalable K-means Bahmani et al. ’12) The answer is K-means++ does not scale. But what is it ? And why does it matter ? Do you have ...
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What explains the peak performance at K=2 for EM initialized with K-means? [closed]

I have a binary classification problem. When I use Expectation Maximization initialize with K-means. What explains the peak ...
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Clustering - Distance Metric for Comparing Short Lists of Terms (non-repeating, no frequency)

Clustering involves using some distance or similarity metric. What is the best way to score the similarity of these small sets of words? Criteria: These are technical terms which are extracted from ...
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R: K Means Clustering vs Community Detection Algorithms (Weighted Correlation Network) Have I overcomplicated this question

I have data that looks like this: https://imgur.com/a/1hOsFpF The first dataset is a standard format dataset which contains a list of people and their financial properties. The second dataset contains ...
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K-Means transform function does not match pairwise_distances_argmin_min centar calculation

I need to be able to select first N most representative points from each cluster calculated by K-means. To do so, I am aiming to calulate the distance of each point to its own cluster center and take ...
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What is the advantage of cluster definition in partition-based and density-based clustering methods?

Partition-based methods consider that a cluster is homogeneous (points are similar according to error sum of squares) and has a centroid. Density-based methods consider a cluster to be a dense region. ...
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Clustering with large number of clusters

I would like to cluster tens of millions of vectors (hidden states of BERT) into something like 20k clusters. Is there a clustering method that can do this in a reasonable time? Standard K-means ...
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What does this array created by KMeans represent?

KMeans is a common clustering algorithm. However, I am not clear about the steps involved. I am using commonly used iris dataset, which has 4 numeric features and 1 Species column. ...
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KMeans clustering - can inertia increase with number of clusters

I am doing kmeans clusters on sales data and i see that inertia increases for the initial increase in the number of clusters. Can you please explain why that happens? I am doing Batched Kmeans for the ...
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Trying to Unskew data with log transformation, unable to get a somewhat normal distribution

Wannabe Data scientist here trying to do some k-means clustering, please go easy on me if there is a really obvious answer :). I'm currently at the step where I'm trying to unskew my data using log ...
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Confused between K-Means and Hierarchical Clustering for 9 different categories

I am trying to classify 9 different species of elephants into clusters using unsupervised learning. I have the following data about them: Their height Eye Colour Sound they produce in decibel (dB) I ...
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K-Means Clustering for telecom customers behavioral usage

I am trying to run K-means clustering on a dataset of 100k records and 26 columns. My problem is in the visualization or plotting clusters part. Since I have several features, I couldn't specify the x ...
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How to identify distinct classes for classification problems?

I'm working with a dataset in which we've taken audio recordings of coral reef habitat from 3 different types: healthy, degraded and restored. From each recordings I have 13 different continous ...
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Can we always get an optimal $k$-means cluster arrangement?

I am currently studying $k$-means clustering. An optimal $k$-cluster arrangement is defined as follows: Fix a distance $\Delta$ and $k < n$. Assume $\mathbb{X}$ have been partitioned into $k$ ...
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Best Way to Get Flattened Sentence Embeddings using Individual Word Embeddings - Glove/Embedding Layer Keras

Okay so basically I have a dense matrix of sentence embeddings within which each word in the sentence is embedded to a dimension of (1 x 100). Sentence embeddings with word embeddings of shape (1 x 2) ...
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Fixing K-means that produces column clusters [duplicate]

Red crosses represents the center of the cluster and the black points represent the data points. I have this hypothetical scenario where the K-means seems like is producing a bad clustering. Why would ...
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K means clustering breakup---galaxy spectrum data set

I have a spectrum data set (total 22000). Similar to an electronic wave data, two dimensional (Flux vs Wavelength). A typical set of wavelength plot looks like below Now I am doing kmeans on this ...
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Is it possible to have same result for knn classifier and kmeans?

Could we achieve similar grouping or results for a set of data, if applied with either Knn and k-means
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How to compare consistency between clustering results and list of values with different levels in R?

I found similar subjects on the website but I may have missed the relation with my own question. I'v seen questions about comparison of clustering results, but here it's more about comparing two lists ...
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In cluster analysis, how does Gaussian mixture model differ from K Means when we know the clusters are spherical?

I understand how main difference between K-mean and Gaussian mixture model (GMM) is that K-Mean only detects spherical clusters and GMM can adjust its self to elliptic shape cluster. However, how do ...
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Clustering algorithm for a coordinate-based matrix

I have $1000$ scenarios, each of which is composed of $5$ users' coordinates $(x_i,y_i), \forall i \in \{1,\dots,5\}$. Now, based on users' coordinates, I want to cluster these $1000$ scenarios into ...
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K Means feature weighting

How does one weight the various variables used in a k-means clustering analysis? By which I mean, how to force the model to be disproportionately influenced by a particular feature over others? One ...
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What does minimising the loss function mean in k-means clustering?

I am learning about the k-means clustering algorithm, and I have read that the algorithm is "Trying to minimise a loss function in which the goal of clustering is not met". I understand the ...
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K-Means output the similar to each other cluster

I am trying to run K-Means on my data set of house price prediction problem. After running it, the output of the model seems wrong because the graphs look the same as each other. This is my code: <...
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What does it mean for the k-means algorithm to be trapped in a local minimum?

I am learning about the k-means clustering algorithm. I have read that one of the characteristics of this algorithm is that it can get trapped in a local minimum, and that a simple way to increase the ...
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K-means Cluster: Between-cluster variation = Total variation - within-cluster variation proof?

I'm trying to reduce down the RHS of the below equation to be equal to the LHS $$\sum_{j=1}^{K}t_j(\mu_j-\mu_T)^2=\sum^t_{i=1}(x_i-\mu_T)^2-\sum^K_{j=1}\sum^t_{i=1}w_{ij}(x_i-\mu_j)^2$$ $K$ = ...
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Clusters merged in Kmean clustering

I am doing clustering data on MFCCs from 100 audio files. I am using Kmean clustering model now, and I found that the clusters would be changed if I input new data into model. Below are the situation ...
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How can I cluster sequential data?

Suppose that I have a sequence of vectors $y_n \in \mathbb{R}^m$ for $n \in \{1, \dots, N\}$. My goal is to divide $y_n$ in $K$ clusters and want my clusters to satisfy the following conditions: Each ...
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SPSS K-means Clustering: “Not enough cases to perform cluster analysis”

As you can read in the title I get the error message "Not enough cases to perform cluster analysis" after trying K-Means Clustering including all the variables (or columns). I will try to ...
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is k-means generalizable at any distance? [duplicate]

The classical version of k-means uses the Euclidean distance in the first step, and the arithmetic mean (the value center) in the second step. Is k-means generalizable to other distances and other ...
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What is the lowest sum of squared errors (sse) of a dataset?

Given $X \subset R^d$ a set of data points, $z \in R^d$ a vector, $SSE(X)=\sum_{i=1}^{|X|}||X_i-z||^2$, I wonder what is the best value of $z$ so that $SSE$ is the lowest? I suspect that $z$ is the ...
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Can DBSCAN overcome the drawbacks of K-means?

We have a great post to discuss the drawbacks of K-means. Can DBSCAN overcome these drawbacks? and what are the drawbacks of DBSCAN?
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K-mean clustering label problem

I am doing K-mean clustering by SKlearn. And I have a question about the clustered labels. Is it possible to keep the same label number if a new cluster is entered? For example, If I have a data set ...
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What is the statistical relevance of gamma in k-prototypes algorithm and why is it related to the standard deviation of the numeric columns?

The k-prototype algorithm uses gamma to provide weight to the categorical features. I have a few queries regarding it : Why is there no upper limit to it? Should it not be (1-gamma) such that gamma ...
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For which clustering algorithms is the Gap statistic useful?

How can i know for which clustering algorithms (with a parameter that represents number of clusters) it makes sense to use the Gap statistic? I've read in the paper by Tibshirani, Walter & Hastie ...
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Do $k$-means, dbscan, and hierarchical clustering all rely on (pseudo)metrics?

I seems to me that the clustering methods $k$-means, dbscan, and hierarchical clustering all work on distance measures $d$ that are (pseudo)metrics, i.e., fulfill the following requirements: $$ d(x,x)=...
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clustering with equal elements

Assume that we have a set of observations: $\mathbf{X} = \{x_{1}, \dots, x_{n}\}\subseteq \mathbb{R}^{d}$, containing $n$ observations for a fixed dimensionality $d$. Assume, we have some fixed ...
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partial_fit API in minibatch K-Means Sci-kit Learn

The documentation on the partial_fit API from Sklearn is very sparse. I am trying to understand how it works with Sci-kit learns Minibatch K-means algorithm: https://scikit-learn.org/stable/modules/...
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Difference between Log Transformation and Standardization

Is there any difference between the log transformation and standardization of data before subjecting the data to a machine learning algorithm (say k-means clustering)? It looks like a common approach ...
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Can maximum likelihood estimation be used for non-parametric models like k-means clustering?

Maximum likelihood estimation considers the likelihood of data given parameters. In a non-parametric model like k-means clustering, can MLE still be used? I know a Gaussian mixture model can use MLE ...
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Relation between k-means for k = 2 and OLS regression [closed]

I'm trying to understand the relation between k-means and OLS regression. Specifically, does the line connecting the means for 2-means clustering correspond to the ordinary least square fit on the ...
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Clustering common words for objects

I am currently running experiments aiming to simulate information transfer between agents. Without going into too much irrelevant detail, following the conclusion of a simulation I am left with a csv ...
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How would one use Voronoi diagrams for KMeans for high dimensional data?

I am reading Aurelien Geron's Hands on Machine Learning, and in the Unsupervised learning chapter he demonstrates how to create a Voronoi diagram after performing K-Means clustering, and produces the ...
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details in X-means algorithm running and Bic

I want to run x-means and needs to make sure I understand fully . I want to simulate X-means algorithm based on [1] in MATLAB. I have some questions about this algorithm. X-means Algorithm Steps: (1) ...
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is there a difference between scaling the data before running kmeans cluster algorithm in r and using scale = true while in the code?

I want to apply kmeans clustering after autoscaling. should I scale the data before running the kmeans cluster code? Or is this not necessary, because there is already a feature "scale" and ...
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K-Means on Time Series but each timestep is considered an individual point

As stated in the question, I have a doubt about the possibility that K-Means would work if we apply it on one time series where each timestep is considered an individual data point. Please allow me to ...

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