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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How to prove that K-Means splits all space into convex polygons?

I want to prove that K-Means algorithm splits the whole object space into convex (probably not bounded) polygons. I've tried to use the fact about convergence of K-Means algorithm and get ...
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Discovering a natural grouping structure in data

I'm looking to apply some data using the sparse-group lasso. This method requires that the variables sit within groups, so I need to pass group labels to the model. Is there an efficient method for ...
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Applying clustering algorithms after t-SNE in R

So I'm doing my bachelor`s work and I'm applying different clustering algorithms on certain data. Before all the clustering of course I'm using a dimensionality reduction algorithm such as t-SNE for ...
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What is the standard threshold value that is best for accuracy when employing Euclidean distance as a metric for gauging textual similarity?

I'm using Euclidean distance as a metric to compare two sentences for similarity while clustering them using my custom incremental KMeans algorithm. The current threshold value I'm using is 0.7 which ...
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What is normalized winning frequency in kernel self organizing map(SOM)?

In the k-means based kernel SOM, proposed by MacDonald and Fyfe (2000), the update of the mean is based on a soft learning algorithm mi(t + 1) = mi(t) + Λ[φ(x) − mi(t)] where Λ is the normalized ...
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Why does this K-Means cluster example show 'overlap' between clusters?

I was reading the hypertools docs and came across this pictorial that shows 10 clusters (some seem to share very similar coloring) generated from some (mushroom) ...
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K-means clustering - weird PCA visualization

I performed PCA on 4 variables and are shown in this visualization: At first look it doesn't look convincing and the some clusters seem weird. The data was cleaned and standardized beforehand. Only ...
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K means clustering of image with k=1 vs mean of all pixels

I have relatively uniformly colored images and I extracted colors using k-means. k means 1 showed the best results for my modeling purposes, k means 2 not so much, and with k-means 3 there ceased to ...
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Method for pairwise ordering two datasets

Given two rather small but unordered multidimensional vectors/datasets (e.g sets of a handful of 3D coordinates), what is a simple method for pairwise alignment/ordering? I've though about using ...
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Elbow method not giving a proper curve in python code

I am trying to determine how many clusters to use for my k-means clustering using different methods. first i used the following code to calculate different metrics per cluster number and different ...
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Termination conditions for K-means and their interconnection

As far as I know, there are two termination criteria for K-means clustering algorithm: assignments of data points do not change centroids do not change I wonder if there is any kind of relation ...
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Mathematics behind standardizing the data points in machine learning algorithms (e.g., K-means clustering)

For K-means algorithm, among other methods using distance-based measurements to determine similarity between data points, why we have to standardize the data points with mean as 0 and standard ...
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Continuous monitoring of KMeans model post production

In the process of deploying a KMeans model for a customer segmentation use case into production. KMeans doesn’t produce the same results every time and after production cluster sizes and arrangements ...
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Proving that K-means corresponds to an EM algorithm?

Just wanted to make sure that my proof is correct and that I am not missing anything in the process. Any thoughts? " To demonstrate mathematically that the K-means algorithm corresponds to an ...
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Can I use kmeans on paired data?

I want to see if a treatment brings patients closer to controls using multiple dependent variables. Can I do kmeans and see if the controls are separate from the patients before treatment, but cluster ...
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Loan Data: Bucket recoveries 1-D array

Some context: When someone defaults on their loan, we keep track of the recoveries as a percentage of the exposure (loan amount), we have a limited time T (legally) to collect recoveries, those ...
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How would you interpret a jagged continually increasing silhouette plot for k-means cluster analysis?

I am running a k-means cluster analysis of textual data (k = 750) and the following is the plot of the silhouette score by cluster I am trying to decide optimal k and I'm wondering how one would ...
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Question about Silhouette index calculation using scikit

I am currently working with continuous data measured from different sensors (thermometers and voltmeters). I have a matrix whose columns represent the sensors and the rows are normalized measurements (...
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Which method should be used if I want to find relations between two variables

Assume that you have a matrix $X \in \Re^{M x N}$ that have $M$ rows and $N$ columns. The rows $M$ can vary in length, but the $N$ columns remains constant. Each row is labeled with a class ID. The ...
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Should main effects and interaction terms be included in Kmeans clustering? (hierarchical principle in clustering analysis)

Let's say I'm trying to cluster observations based on five features, including: n_emps: Number of employees n_cust: Number of ...
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Assessment of Geospatial Analysis and Poisson GLM Modeling for Accident Frequency with Cluster-Based Features

I have a serious doubt about the quality of this approach: Firstly, a geospatial analysis was conducted using accident frequency and wind speed data to segment the map of a country into clusters using ...
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Turning heatmap into clusters - Classification

Assume that you having a heatmap that looks like this. The goal is to classify all the "dot" inside the image. How can that be done? The assumptions of the image: The image has always black ...
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Criteria for a Distance Metric to be Compatible with K-means Clustering

Referring to this post, it's mentioned that K-means clustering doesn't inherently rely on the pairwise distances between data points, and not every distance metric is suitable for k-means clustering. ...
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In unsupervised learning, is a result of 2 clusters meaningful?

I used both agglomerative clustering and k-means on a dataset and see the results below. Result from agglomerative clustering was demonstrated with silhouette score while kmeans with inertia score. ...
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Method to find group associated with a target variable [closed]

The business question that I am trying to answer is: what group(s) of people have the highest chance of default? The features that I have are income, debt to income ratio, fico, etc. How do I find the ...
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Would it be wise to feed k-means results into a cmeans?

I am conducting a cluster analysis of documents using document embeddings as the input to the algorithms. One of the problems I am coming across is that in reality there are documents that belong to ...
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How to tell whether segments from K Means clustering result are "successful" and will impact business metrics?

Background I'm a data analyst. The Business unit I'm assigned for needs to segment users based on power vs non-power users so they can target each segment with proper treatments. Goal Segment users (...
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Dummy Variable Trap in KMeans Clustering

My data set is having a column Gender, so I have to apply One Hot Encodingto perform KMeans Clustering. Q1. Should I take care about ...
mainak mukherjee's user avatar
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Clustering algorithms puts data points that are visually far apart in same cluster

I am trying to cluster a very large set of data points, of roughly (20000, 100) shape. I could not run density based DBSCAN or SpectralClustering due to the ...
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Interpreting results of K-means after PCA

I have this dataset about an airline company customers with 22 explanatory variables. My goal is to perform some sort of customer segmentation with the k-means algorithm. One problem that I've found ...
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General technique for loss function minimization

I was trying to rationalize the K-Means algorithm and came up with the following thoughts. Suppose we need to compute: $T=min_x L(x)$ but we struggle because $L$ is complex. Suppose we find $L'$ s.t.: ...
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Finding patterns of volume worked - Productivity Mix Correlation

I am trying to generate labor standards (the amount of productivity per hour an employee should be accomplishing) based off of historical data volumes. I do not have the start and end time that a ...
racurry1993's user avatar
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Elbow method Vs Gap statistics, which one? challenging for data scientist

I am working on hourly-weather data. It contains four features: rain, wind speed, humidity, and temperature. Obviously, all of them are continuous values. The number of records is around 17000. Other ...
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Can I use K-Means to group customers based on a single variable?

I have a test dataset of 11m records. The dataset contains a global customer id and spend figure. I need to group customers into the following categories: 0 Low 1 Low/Med 2 Med 3 Med/High 4 High I ...
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How to identify the clusters in SSE plot?

How to determine the number of clusters from the following plot?
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Unsupervised learning: How to identify differences between clusters?

I'm learning about unsupervised learning and I tried to use KMeans, AgglomerativeClustering and DBSCAN on the same datase. The result was ok, they seems to work fine according silhouette_score() ...
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Does it make sense to transform a feature containing hours (24h) into two features with xy-coordinates of each hour in the space? [duplicate]

I have a clustering problem that I might solve with an algorithm based on Euclidean distance (e.g. K-Means). One potential feature is the "hour" at which each user began an interaction. As ...
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How do I choose k for k means clustering [duplicate]

Given a set of points, I'm trying to find the right cluster. However, I am lost on what the process is. Here is the graph of all possible points. I am unsure what I should look at
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Choosing the best clustering algorithm and evaluating the results

I'm trying to separate my data into clusters using the k-means algorithm and the hierarchical algorithm, choose which algorithm fits my data the best, and evaluate the results. However, all of my ...
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How to interpret the Scatter Plot result from PCA? [duplicate]

I have a project in school about clustering analysis. I have applied standardization and principal component analysis (PCA) to my dataset (I used K-means), which is about heart disease patients. I ...
AK6000W's user avatar
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In $k$-means, how is it NP-hard if the dimensionality of the data is at least $2$ ($d\geq 2$)?

In $k$-means, how is it NP-hard if the dimensionality of the data is at least $2$ ($d\geq 2$)? Can someone justify or give reasons to this statement? Any guidance would be appreciated.
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K-means on linearly projected features

I am looking for references on K-Means applied to linearly projected features instead of to the original features, in the sense that both K-Means and the projection matrix are learned at the same time....
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Can K-means put most of the noise in the same cluster?

I am working on clustering text data (very short sentences) vectorized with tf-idf. The data are characterized by high sparseness and the presence of abundant noise (considered here as documents that ...
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How can I ensure both levels of a binary variable are represented in every cluster?

Let's say I have some continuous variables and a binary treatment indicator. I want to cluster my observations based on the variables while ensuring that each cluster contains at least one member of ...
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How does this prove that the objective function in K-means clustering never increases?

I am reading the ISLR textbook (pg. 518-519, 12.4) and having trouble understanding why K-means clustering never increases. I can understand it conceptually but I don't understand the mathematical ...
idkmath28's user avatar
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Time series clustering on large data

I am trying to do K-means clustering on my data which has time series length of 3700 and for (latitude,longitude) points of around 6000 in length. However, timeseries clustering using tslearn package ...
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SPSS: how to calculate nearest neighbor from k-means centroids

the subject of my problem is related to cluster analysis. Specifically, with SPSS I am conducting a cross-validation procedure based on splitting the sample data into two independent halves (subsample ...
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Can the loss of KMeans increase?

Let's say we have a dataset, and we run KMeans with k clusters. Is it possible that during the execution of KMeans the loss first decreases and than increases until ...
holo gram's user avatar
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Normalization/standardization impact on T-SNE and K-means

I have a dataset of 20K samples on 27 features that I am trying to cluster with k-means. The dataset is in its majority rather sparse, i.e. 98% of samples have a single nonzero value in one of its ...
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PCA : how to cluster data to differenciate my data the most while considering their groups

I have to do a PCA in R for a project, but I have 300 data in 15 differents groups, and I want to find the reduced space which gives me the most variability between the groups and cluster my data in ...
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