Questions tagged [k-means]

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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Add 'other' as one group to clustering algorithm inference pipeline

I have few clustering algorithms tuned having 5 cluster. I want 6th cluster if new data does not belong initial 5 cluster fall in 6th cluster. 6th cluster [ say other category] consist of all data ...
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Using variance Threshold removes all my features (clustering)

I have 100+ features for clustering. I am unfamiliar with unsupervised tehcniques for clustering. I have heard of variance threshold. However the features i have used have low variance.. e.g. some ...
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I know which forums specific user read - how to cluster the data?

I have a dataset as follows. For each user, I have a separate row with the forum he reads. There is up to 100 different forums. I would like to cluster this data, so each user will be assigned to one ...
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16 views

Understanding clusters after applying PCA then K-means

I have a dataset grouped by customer level, and the rows are sum_mexico, sum_uk, ... etc to indicate if the customer has spent money at stores in those countries..similarily counts for these as well. ...
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Perform clustering from a similarity matrix

I have a list of songs for each of which I have extracted a feature vector. I calculated a similarity score between each vector and stored this in a similarity matrix. I would like to cluster the ...
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37 views

Original k-Means Research Paper

I'm having difficulty searching for the original published paper proposing k-Means as an algorithm. I have been inspired to find it as reference for similar work, inspired by this TowardsDataScience ...
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Explaining the similarities between instances in a cluster with KMeans

If I create clusters using the KMeans clustering algorithm in Python, is there any way I can find which attributes were used to group those instances in clusters? Example: I have a dataset of cars ...
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K-means related question

I am a new user to Matlab kmeans and have the following question: I would like to use the following call from a Python application which is listed in this URL. ...
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17 views

Create clusters based on specific keywords

I am working on raw text data. I am using clustering to put together common words in the documents. My requirement is to create clusters based on a specific list of words i.e I want to get a group of ...
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When should we choose agglomerative clustering over K-means clustering?

I was working on a clustering based model and I read about hierarchical clustering and K-Means clustering. Under what conditions should I choose agglomerative over K-means clustering?
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Which algorithm would be suitable for clustering a billion datapoints?

I am running a K-means algorithm (using the sklearn implementation) on an aggregated dataset of ~350k datapoints on a 6 dimension hyper-plane (using 6 features). I ...
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19 views

How does the inertia cython implementation in scikit-learn for kmeans work? [closed]

Specifically, what do the & symbol stand for? and why is the column index always 0? ...
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Compute similiarty between labels

I have a labeled dataset and I created a duplicate of this dataset and removed the labels and applied K-means clustering with k= the number of labels in the original data set I want to compute ...
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50 views

Unsupervised Text Classification with Python: Kmeans

I am working on a project to build a text classifier of questions being asked. There are no labels provided in my data so I have chosen to go with an unsupervised approach. This solution needs to read ...
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22 views

what is the difference between strong and weak clustering?

What is the difference between strong and weak clustering? and what algorithm is considered as strong and weak clustering? Is fuzzy c-means and bisecting k-means considered as strong clustering? I ...
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What is the optimal method to evaluate clusters further?

Supervised learning is straightforward on medical data using Orange, but unsupervised learning is more challenging. I selected a data set based on Florida County Health Ratings where individuals rated ...
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27 views

Can distortion be derived from inertia rather than recalculating it from scratch in case of kmeans?

I got this definitional difference between distortion and inertia from here: Two values are of importance here — distortion and inertia. Distortion is the average of the euclidean squared distance ...
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Considerations to take into account when clustering

My idea is to use clustering to perform stock segmentation based on risk, building different risk levels that might adapt better to different kind of users. Hence I have computed different risk ...
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Feature scaling for clustering

I want to cluster groups, using K-Means, DBSCAN, etc. algorithms, based on lat-lng coordinates along with other features such as dummy variables, continues variables (in different units). What would ...
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Clustering of longitudinal user generated data; determine at what point in time does the user “become” the final clustering outcome

I analyse a lot of telephone call log data sets (akin to user generated data) and I use k-means clustering a lot to look at the types of callers that exist in the data set. The callers are clustered ...
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Trouble understanding k means distance calculation with high dimension

If I have a dataset with 10 features, 100 samples, and set I k = 3, three rows will become the 'centroid'. However, if I have 10 features how is the centroid calculated i.e. what exactly is it? ...
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kmeans cluster results for use as xgboost feature

I am curious if kmeans clusters can be used as xgboost features, along with the original features? Specifically for features X and labels Y can we: Split into X_train, y_train & X_test, y_test ...
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24 views

Clusterize item set with items as vectors of features

I have to clusterize this dataset in which I have houses and water consumption in this form: $$ House1 = (x_{1},x_{2}... x_{n});\\ House2 = (y_{1},y_{2}... y_{n});\\ House3 = (z_{1},z_{2}... z_{n});\\ ...
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How do I calculate distance of test data point from centroids in KMeans scikit-learn?

I am using Kmeans clustering on my data After training the model, I want to calculate the distance between Test Data points and CLuster centers. How do I do it? ...
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How is k-means implemented in CNN?

I am new to CNN and want to try implementing YOLOv4 with a custom dataset for vehicles. As I understand it, k-means clustering is done to give labels to a set of data. I was going through some papers ...
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What's the best way to detect crowds?

I have a dictionary containing people and the distance between each pair in the following format: ...
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57 views

How to interpret results of a Clustering Heart Failure Dataset?

I am doing an analysis about this dataset: click In this dataset there are 13 features, 12 of input and 1 is the target variable, called "DEATH_EVENT". I tried to predict the survival of the ...
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70 views

Why the Silhouette Score and optimal number of Cluster changes when using 2D and 3D data?

I am experimenting with Kmeans clustering. My data (vectors) was in 300 dimensions which I am converting into 2D and 3D using PCA. Now, to find the optimal number of clusters, I used the Silhouette ...
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25 views

Is knn similar to this version of k-means?

If we use k-means in a dataset where k is equal to the number of points in the dataset, and each cluster is made out of only a point. Considering that we have given a distance method, we can classify ...
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44 views

Can we automatically chose k value in k-means algorithm?

Can we choose automatically the K value, trying every possible values (k=1,.., n) where n is the number of instances to be clustered. We then keep the value of K for which we obtained the minimum ...
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49 views

how to compare between kmeans and hierarchical clustering results

I am using 2 types of clustering algorithm I apply hierarchical clustering the K-means clustering using python sklearn library Now the results are a little bit different so how can I compare the ...
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What does MiniBatchKMeans fit's reassignment_ratio parameter do exactly?

I am using scikit-learn MiniBatchKMeans to do text clustering. In the constructor method, there is a parameter reassignment_ratio, which is described in the ...
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49 views

How to interpret the sample_weight parameter in MiniBatchKMeans?

I am using scikit-learn MiniBatchKMeans to do text clustering. In the fit() function there is a parameter sample_weight described as follows: The weights for each ...
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22 views

k-means for customer review analysis

I have a dataset of amazon Alexa reviews and want to group negative and positive reviews in separate groups. Is k-means a good approach to it? The dataset is unlabeled so how will my model know which ...
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159 views

Differentiate between positive and negative clusters

I have applied k-means clustering on my dataset of Amazon Alexa reviews. ...
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76 views

Understanding and find the best eps value for DBSCAN

I'm trying to run the DBSCAN algorithm on this .csv. In the first part of my program I load it and plot the data inside it to check its distribution. This is the first part of the code: ...
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How to approach Customer dimensions and work hours relation problem

I am thinking about how to solve the following problem related to a supermarket and its employees' workload prediction: I have customer dimensions (attributes): CustomerID Loyalty status Uses e-store ...
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33 views

Visualising K-Means clusters for 3D data in R

I have an excel file that contains 485k rows x 3 columns of integer values. Sample data: ...
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35 views

K-means clustering to separate temperature vertical profiles

I have temperature measurements from weather stations in a mountainous region and I want to obtain a vertical profile from these data at any given time. In a simple case one can just plot all values ...
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47 views

How to cluster user historical data? [closed]

I have transactional-level users data that includes their behaviour, such as reading articles, searching for content, posting, etc. I would like to cluster (most probably K-Means) these users based on ...
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39 views

Can we combine multiple K-Means Models as a single model?

I have a NLP problem statement where I use a Word2Vec embedding pre-trained model to convert key text to vectors and then on a set of terms run k-means clustering to get a final model for certain <...
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88 views

K-Prototype for anomaly detection

I have logs of the form (e.g. from a gym login).. the representational case is so: UserName, Login time, timeSpend_on_weights, time_spent_on_elliptical ...
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Why kmeans cluster breakup is like this [closed]

I have a galaxy 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 ...
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How to get a KNN model (using quantiles to scale variables due to non-normal distributed data) to be better suited for non-extreme values in the data?

I want to cluster my data via k-means/modes. As the variables in my data are not normal distributed, I am not using the z-transformation to scale my data. I am scaling my data by categorizing each ...
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115 views

Should normalization be applied?

I have more then 100 columns with the values of 1-0. But the two features at the end as seen in the below image, have different values then the rest. Should I rescale the values in the last two ...
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20 views

Clusterize Spectrum

I have pandas table which contains data about different observations, each one was measured in different wavlength. These observsations are different than each other in the treatment they have gotten. ...
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30 views

Similarity matching between two distinct datasets (marketing case study)

I am working for a company that sells different products to customers. My objective is to find customers that are likely to purchase product X based on the profiles of customers that already purchased ...
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67 views

DBSCAN Clustering

I used K-means to get the number of clusters for my data(Elbow Method). Then I was trying to see if for some specific hyperparameters can we get the same number of clusters for DBSCAN. I tried Brute-...
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41 views

Why Davies-Bould chose a number ob cluster higher than Silhouette or Calinsky Harabasz?

I am doing use several metrics in order to know what number of clusters is correct in order to do this I selected 3 clustering algorithms and 3 internal evaluation metrics, Silhouette, Calinsky ...
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What criteria use in order to select the best internal validation for clustering?

I am doing homework about how to evaluate a clustering algorithm both hierarchical and partitional. For doing this I have a dataset that I can plot as you can see: The clustering algorithms that I am ...

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