All Questions
Tagged with clustering k-means
744 questions
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Clustering and data scaling
I have a dataset with 5 questions, which are scaled 1-10 and income variable, which is nominal.
Should I standardise all variables with min/max scaler, or convert income to 1-10 scale?
What is the ...
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238
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How to optimize K-means to eliminate outliers and unrelated clusters?
I clustered document embeddings with K-Means. Embeddings have 2048 dimensions. Now, i am trying to optimize clustering. There are two problems. 1- Some clusters may have outlier samples. 2- Sometimes,...
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173
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Silhouette score: counter-intuitive results
so I was looking back at this tutorial (https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) and it struck me that the example with ...
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How to "characterize" clusters (e.g K means)
I ran the K-means clustering algorithm on the iris data using the R programming language:
...
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Using Silhouette Score to evaluate different clustering algorithm
I am trying to compare different clustering algorithms on a dataset and compare the model performance. Since the dataset is quite big (56 features), I applied PCA to reduce the number of features to ...
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240
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K-means clustering and splitting further
I have a problem with my current project. The goal is to create clusters of customers with homogenous properties. So, each cluster should contain a group of customers with similar attributes.
I ...
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181
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Why is it wrong to apply k-means to a distance matrix?
There are several threads discussing clustering analysis of a distance matrix and they dismiss use of the k-means algorithm. Here are two examples:
Perform K-means (or its close kin) clustering with ...
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489
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K-Means clustering - upper bound for number of iterations
Suppose we run the K-means clustering algorithm on a one-dimensional dataset, i.e. $p = 1$, so that each observation consists of a single real number.
We assume that these real numbers are distinct.
...
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405
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KMeans where there are clusters of data with different densities
Suppose we have $n$ data samples and they are grouped in two sets such that half of the data are in a high density region and the other half is in a low density region. These regions are apart from ...
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The total within sum of squares gradually decreases in K-means algorithm
Show that for K-means algorithm,
$ \sum_{k=1}^K \sum_{i \in C_k} d(X_i,\bar{X_k}) \ge \sum_{j=1}^K \sum_{i \in C_j^\prime} d(X_i,\bar{X_j^\prime})$,
where d is the squared Euclidean distance,$\bar{X_k}...
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122
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Weighting k-means with two attributes
I am aiming to use K-means to cluster lat-lon points, but I want to apply a weight to each point's distance based on two attributes of the point.
Attribute 1 is population and attribute 2 is percent ...
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124
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KNN and K-means, very different but possible equivalency?
Why does the k-nearest neighbor algorithm and k-means clustering algorithm with $k=1$ act the same?
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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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How to validate a confirmatory k-means cluster analysis in SPSS?
I've conducted a k-means cluster analysis in SPSS using the Z scores of two continuous variables for which the number of clusters was known a priori and the total number of observations exceeded 2000. ...
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Clustering Highly Skewed and Segmented Data
So I am trying to cluster a dataset that looks like the following:
I have tried K-Means and GMM, which give me horrible results. I have tried DBSCAN, which was okay, but it is difficult to choose the ...
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Why would my additional information harm my prediction score but improve ROC and F-1?
I'm trying to predict the primary crime type on a given location using the Chicago crime dataset.
Stripping out all the provided features to just:
Location Description Encoded (The location ...
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199
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How to evaluate the perforamance of clustering model using python
I have implemented the k means clustering model using python , i would like to know whether my model is perfect or not , so that i want to know which performance metrics is used for clustering model ...
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232
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Which is the best clustering algorithm for clustering multidimensional data with low density difference?
I am working on a project currently and I wish to cluster multi-dimensional data. I tried K-Means clustering and DBSCAN clustering, both being completely different algorithms.
The K-Means model ...
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195
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How do I interpret K-means model output from Pyspark ml. clustering library?
I am new to PySpark and learning how to build models using PySpark's machine learning libraries. I build a k-means clustering algorithm based on the code of this website. Now, I fed my data into the ...
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Visualize Analysis of clustering after pca
I am using kmeans for clustering and if I read the topics around here and somewhere else it is always recommended to do a graphical check-up for the number of ...
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342
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Is it correct to use permutation testing to find the significance of a clustering solution?
I am performing exploratory clustering with k-means in a multidimensional (82x18) dataset.
The algorithm that I am using tests several number of clusters and uses several goodness of fit metrics to ...
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64
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k-means method clarification
I am pretty new to k-means and cluster analysis methods, but I am trying to do it on 5 different measures of inequality and redistribution (Gini, P90/P10, Atkinson with different parameters and the ...
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377
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Can a k-means cluster use different measures for homogeneity and heterogeneity?
TL;DR: Is there an existing k-means clustering algorithm that can have different weights for the (minimized) in-group distance measure and the (maximized) between-group measure? Or, better yet, can ...
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154
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k-means clustering issue voice data
I'm getting an issue in my k-means I don't know if it my data-set or what anything else.
Why i got thia flowing point in the right side of the image?
...
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252
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Using k-means clustering to train radial basis neural network for highly imbalanced dataset
I am trying to find prototype neurons for my radial basis neural network. My dataset has 30 attributes (of which 28 of them are the result of a single PCA) and 300.000 observations. It is a binary ...
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553
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Can Silhouette score compare algorithms based on different metrics?
If I intend to compare the clustering performance between K-Means and K-Modes clustering using this measure. How do I do so? y data set is binary in nature and I want to see if K-Modes using Manhattan ...
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2
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5k
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Clustering Data Using Gower and Kmeans
I am trying to do clustering on my data which consists of both categorical and continuous variables. I have some questions which I would like to ask:
I am going to use the Gower Distance measure to ...
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244
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How to interpret contrasting information from the Variation of Information, Dunn and Rand Index for comparing clusterings
There are related questions but the answers don't seem to explain how to practically judge these measurements for non stats users.
I have a dataset which I clustered with K=4 using hierarchical ...
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148
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Using K-Means to cluster vectorized user reviews
I have a dataset of user written reviews. What I would like to do is to cluster similar reviews together.
I have already trained a word2vec model on vocabulary of 50000 words, and I have updated my ...
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99
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How to make 65 clusters ? Is k-mean good algorithm to do this?
I am trying to segment customers based on demographic, behavioral, lifestyle etc into 60-65 segments inline with Claritas Prizm segments Link1 Link2
I have 1 million records and 264 variables. ...
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What is the interpretation of K-means clustering on a weighted adjacency matrix?
Suppose I have a weighted adjacency matrix A representing a graph G. I use K-means on this matrix to group vertices together.
What is K-means finding exactly? I mean, what is the interpretation of ...
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What are differences between K-means versions: Lloyd, Forgy, Macquen, Hartigan and other?
I'm looking for the (perhaps brief) explanation of the main differences between the different K-means clustering procedures, such as between Lloyd, Forgy, MacQueen and Hartigan, and possibly other ...
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K-means clustering when k is initially 1 during training but becomes 2 as more instances arrive
I have a dataset or both anomalous and normal instances. These data instances are arranged based on arrival time from January 1st until December 31st.
I want to cluster these data points, but the ...
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35
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K-Means and clustering
I am doing a course about machine learning this semester, and while reading a tutorial I came across this question:
\begin{align}
X &\sim U[0,d] \\
N &\rightarrow \inf \\
\delta &\...
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48
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kmeans: Question about feature values
In an example about kmeans for exploratory analysis the instructor examines the centroids and affirms that the centroid coordinates with the highest values are those that "drive" “belonging” to that ...
user110848
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666
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Tuning KMeans K paramer - pitfalls and general guidelines
I'm clustering data with true/external labels, call the number of such groups K0 where K0/n is close to 0.01. However I also need to be totally agnostic to the validity of these labels.
Call my ...
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208
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Where can I find Image(RGB) data to implement K means agorithm? [closed]
I am a newbie to Data science.I have learned about clustering algorithms(especially K-means algorithm)and I would like to implement this algorithm(using Euclidian distance metric) to segment image on ...
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304
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xmeans varies the number of clusters
I've programmed the kmeans++ algorithm in java, it's the regular kmeans but choosing the initial clusters in a smarter way rather than just random. I've made some tests and this seems to be correctly ...
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712
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can I use manhattan distance function on hartigan wong kmeans clustering
I would like to perform Hartigan Wong clustering on high dimensional data. As I understand, Manhattan distance works better than the Euclidean distance in higher dimensions.
I have been using the K-...
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70
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How can I use the results of clustering algorithms for classification
I'm doing a mobile customer segmentation and I was using K-means to cluster my data according to the various data points (location, time of use, duration used for etc). After reading a lot of posts in ...
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KMeans With $K = N / 2$
I have two data sets of the same data coming from two different sources. I am trying to match all of the rows in one data set with the other by comparing several columns in each set. Each data set can ...
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How to decide the initial coordinates of centroid while doing clustering (such as in K MEANS)
Suppose we are looking at a scatter chart and from there, by visual inspection, I would like to pick up the coordinates (initial centers) based on the density of the points. Whats are options or ...
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Which ways should be performed detecting outliers before k-means clustering?
I will make k-means clustering for a segmentation project.But I know that this algorithm is effectable from outliers.Which way should I perform for detecting outliers before doing k-means algorithm? ...
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252
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Theano K Means with Shared Variables and Scan
I have a pet project to reproduce some common clustering in theano in order to improve my understanding for future projects. I was wondering if anyone has ever used nested theano scans on shared ...
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k-means clustering generates either all-male, or all-female clusters
I am using k-means clustering for a problem of market segmentation. My variables are gender, age and other categorical data. I first standardise age and then make all the rest dummy. The other ...
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Using Random Forest classifiers post K-Means for Cluster Feature Importance
I was wondering what the caveats were to the following process for describing clusters produced via K-Means with Random Forests:
Create clusters of data via K-Means
Train K random forests with ...
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93
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Find data shapes by clustering
I am trying to use clustering on my data but I do not have found the results I hoped for.
I have a massive dataset with fire incidents. I would like to find clusters in these data. I want to use 4 ...
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483
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How to evaluate k-means considering initial conditions when having the ground truth?
I use kernel k-means algorithm with different kernels and want to see which one is the best. The way i do it is to fix the number of $K$ equal to number of classes (ground truth) and check the ...
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71
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K-means clustering gives bizare result
I have small dataset of 15 points. K-means clustering 2 time gives me this result.
Besides the random initializing the centroids, what could a reason for this bizarre graph(1st one) that it has given?...
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strategy to determine number of groupings kmeans
Can you offer advice on a clustering strategy?
I have 4 continuous variables and I would like to perform cluster analysis.
The correlation matrix of the varaibles is
...
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