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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Clustering (unsupervised learning) for uneven classes

I am looking for an unsupervised method that can see also the points that start to look different from the majority. Which clustering techniques (I use python) can be used for such data sets? I have ...
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Applying and Visualizing k means clustering on a data set that has 9 features

I had a data set of images that I have extracted 9 numerical features that I want to apply k means clustering or hierarchical clustering to. I'm just not sure how to go about it. The tutorials I have ...
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How to compare different similarity measurements in text clustering?

I have a dataset which contains vectors generated from subtitles (each column represents a genre, each row is a movie name), my purpose is to find the most similar movie titles, I want to use ...
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Anomaly detection using k-means clustering in Python

I'm working on an anomaly detection task in Python. Datasets regard a collection of time series coming from a sensor, so data are timestamps and the relative values. In order to find anomalies, I'm ...
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1answer
17 views

Find shared properties of a cluster samples

I have a dataset which contains ~15 features. With the elbow method, I found out that the optimal number of clusters is probably four. Therefore, I applied the K-means algorithm with four clusters. ...
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3answers
31 views

If I have to recommend 10 movies to the users

Let's say I have some information about a user and movie data similar to the following: ...
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2answers
48 views

Is there a real life meaning about KMeans error?

I am trying to understand the meaning of error in sklearn KMeans. In the context of house pricing prediction, the error linear regression could be considered as the money difference per square foot. ...
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42 views

Can Anyone Explain this code piece by piece? [closed]

Function that creates a DataFrame with a column for Cluster Number ...
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26 views

Scaling negative and positive variables when performing a k-means cluster analysis

I'm looking to perform a k-means cluster analysis on a set of data that contains variable ranges that contain both positive and negative values. Given the rangers vary so much the data will need to be ...
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good algorithm for outliers detection

I have 2 independent data sets (1. 300 rows and 2.3000 rows) with 6 months trades observations for 50 traders. In both datasets I have: trader id, stock title, buy/sell volume, date of trade, sector ...
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Would K-means be Appropriate to Use with Four or More Variables?

Just a general question that I'm trying to mentally visualize. I'm fairly new to using k-means clustering and have used it before on two variables, which creates a 2-D plot of points. I also know, ...
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39 views

Why does changing the cluster number change the plot in Kmeans?

This might be a dumb questions but I can't find the answer to it. I don't have the perfect mathematical understanding of kmeans, so apologies if it is. I'm just wondering why I see a different plot ...
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Product Prediction to group of customers

I have multiple groups of customer, say for segment 1 as shown in the pictures, I have a list of products that I can choose the cross-sell to that group. Consider ...
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38 views

Stationary time series for clustering algorithms

I have a set of time series data that I would like to feed into a clustering algorithm (like k-means, using dynamic time warping as the distance function). After standardizing the data with mean 0 and ...
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Anomaly detection k-means in Time Series

I'm trying to use k-means to detect anomalies in the Amount column. I have the following part of my dataset: ...
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2answers
37 views

Standardization After PCA for Kmean clustering

I want to apply Kmean for clustering after PCA dimensionality reduction. I have standardized data with StandardScaler before the PCA, then I want to train Kmeans for finding clusters. However, the ...
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26 views

k means clustering when k = n

I want to find the minimum value of the objective function if we set K equal to the number of samples. I know the objective function is $J=\Sigma_{n=1}^{N}\Sigma_{k=1}^{K}r_{nk}||x_n-\mu_k||^2$ And ...
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Implementation of Bhattacharyya distance for filtering images that are far off from their cluster

I need assistance with the python implementation of Bhattacharyya-distance for the below use case: Here, the polygons P1, P2..Pn refer to the different images where each pixel is represented by 'n' ...
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k-means classifies one data point as a group

I have 1000 sets of one dimensional data (360 each in length), and I want k means to classify what is a small/medium/large value (n_clusters=3) for each set of data, but I'm getting a lot of instances ...
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1answer
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Clustering of Weekday Weekend Time Series Data

I have a dataset of the number of steps people take throughout a day over a period of months. I aggregated them so that each person will have an average weekday and weekend time series of steps. An ...
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48 views

Creating better features for clustering

I am trying clustering for the first time trying to separate my user into three categories (or the categories I though that they will should fall in). First of all I have two tables that describe ...
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“Memory Error” - Kmeans in python using pandas DataFrame

I am trying to predict on my "dataset_to_predict" having size of (297000 x 5120). While Memory usage is under 50%. No Specific Error message. I'm trying to find # of k using elbow method - Got ...
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How to find anomalies/outliers in Panel Data?

I have panel data based on 900000 different entities with 384 time steps and the data is not normally distributed. I am looking for outliers/anomalies, this is unsupervised as I have no examples of ...
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131 views

K-means clustering for one class classification

I want to know if I can use the k-means clustering algorithm for a one class classification (as in the case of one class SVM), which means I have data for 2 classes, and I labelled only the one class ...
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1answer
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Preparing dataframe to carry k-means clustering [closed]

Im trying to apply 3 different algorithms of clustering on my dataset. to check which one fits the best. I'm confused how should I convert my dataframe -k-means -DBSCAN -hierarchical clustering ...
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Is the mean shift algorithm adapted to my problem?

I'm currently building a model that can detect abnormalities in a timeserie. First, we predict the next steps and then we compare the prediction with what we measure in real time. We want to see if ...
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what could this mean if your “elbow curve” looks like this?

This is from running kmeans clustering with k on the x-axis (ranging from 2 to 10) and the silhouette distance on the y-axis. Clearly there's peaks at k=3, k=4 and it seems to decline from there. It ...
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How to analyze similarity of static K-Means Nodes?

I have performed K-Means(40) clustering on customer product purchases. I am pairing this data with Association Analysis (sometimes called market basket analysis) results to motivate cross-selling ...
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Will the features in the image (edge, color, etc.. ) impacts on the performance of the spherical k-means?

I am very new in Machine learning, I recently implemented the spherical k-means, but finally I found a interesting point from the result. I used four datasets, they are minst, cifar-10, fashion-minst, ...
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1answer
136 views

How to measure the similarity between two images?

I have two group images for cat and dog. And each group contain 2000 images for cat and dog respectively. My goal is try to cluster the images by using k-means. Assume image1 is ...
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1answer
131 views

Kmeans clustering with multiple columns containing strings

I have the following dataset: https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset What I want to find is clusters based on imdb score per genre per country. I have created a pandas data frame ...
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Overfitting in K-means

How do you test your results for overfitting in a k-means run? Some people have said use a training set. I have about 1500 records and about 20 fields.
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1answer
60 views

Randomstate and kmeans issues

I try to cluster a dataframe of 227 rows in 5 clusters using kmeans algorithm. Each time I run my code I got different labels and different clusters which make my analysis afterwards a bit tricky. ...
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1answer
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Difference between cluster centers and means

The following is the output of the cluster centers I got from a cluster model (kmeans - 6 clusters) 3.371069, 3.920354, 3.629747, 3.700000, 3.988506, 3.740385 However, after segmenting the data ...
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k modes: optimal k

I have categorical data and I'm trying to implement k-modes using the GitHub package available here. I am trying to create clusters in my (large) dataset of say, 5-7 records, each of most similar ...
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50 views

What does Make Density Based Clusterer in Weka do?

In Weka, there is a clustering algorithm with the name as Make Density Based Clusterer. When going through its properties, it takes a clusterer as base clusterer(I took it as K-means with k=3). It ...
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1answer
124 views

How to calculate mean and standard deviation of all features in a class identified by k-nearest neighbors?

I have classified my data into several neighborhoods using k nearest neighbors. I need to efficiently calculate the mean and standard deviation for all features of data points belonging to a ...
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1answer
147 views

K-modes implementation in pyspark

I'm looking for an implementation of k-modes in pyspark. I found this and this as implementations. First, I tried implementing k-modes using the first link and faced issues. So I went ahead and tried ...
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Best classification technique for following kind of data set

I have a large table where each record or row represents a single salesperson, and there are 50 columns or dimensions where each column represents one of 50 products potentially sold by any given ...
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1answer
43 views

Use output of K-Mean for Logistics regression

I've created a binary classifier using K Mean, which predicts fraud and legitimate accounts, 0 and 1. This uses two features, let's say, A and B. Now, I want to use other features like C and D, to ...
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1answer
28 views

Scikit learn kmeans with custom definition of inertia?

I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results). However, kmeans uses the sum of squared differences. I would like to be able to input ...
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where does kmeans store its trained model parameters in scikit learn?

Like in linear regression, there is model.intercept_ and model.coef_ but in kmeans i found ...
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119 views

Categorical data into numeric in excel

I have a large dataset and I would like to convert these categorical data into numeric in binary form to perform k means clustering in R. However, I get an error in value. This is the formula that I ...
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4answers
57 views

After running K-means on 12 features, I get an array containing clusters for each row. What is the next step after this?

So I used the elbow method to identify the optimal number of clusters, i.e. 4 in this case. After running K-means on dataset with 12 features, I get an array with the cluster number each row belongs ...
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2answers
501 views

KMeans vs. DBSCAN

I am trying to understand some basic clustering techniques. What is the main difference between KMeans and DBSCAN? Can we use both techniques for the same problem?
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208 views

Kmean clustering on text data

I have a large raw dataset on crime and I want to cluster the data using k-means, However, I get an Error when I enter this code: ...
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1answer
58 views

K-Means visualisation problem 8 numerical feutres [closed]

Hello im looking for exemple with python for K-Means clustering when i have data set with more than 6 feutres. thanks
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282 views

K-Means clustering - What to do if a cluster has 0 elements?

I'm writing code for k-means clustering. I have around 100000 vectors of size 128x1 (SIFT descriptors). I'm trying different initialization methods such as Forgy and Random Partition. What if suppose, ...
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170 views

Should unique vectors (SIFT descriptors) be used in K-Means Clustering?

I'm doing image classification by extracting SIFT features, clustering them and then finding BOVW histogram and classifying. I have around 180 training images from which I'm extracting SIFT ...