K-Means | K-Means Clustering using MapReduce

by himank Java Version: Current License: No License

X-Ray Key Features Code Snippets(4)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | K-Means Summary

K-Means is a Java library. K-Means has no bugs, it has no vulnerabilities and it has low support. However K-Means build file is not available. You can download it from GitHub.

K-Means Clustering using MapReduce

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K-Means has a low active ecosystem.

It has 63 star(s) with 62 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 2 have been closed. On average issues are closed in 344 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of K-Means is current.

Quality

K-Means has 0 bugs and 0 code smells.

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K-Means has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

K-Means code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

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K-Means has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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Main entry point
Demonstrates how to upload a single line

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K-Means Key Features

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K-Means Examples and Code Snippets

title: K-means clustering tags: algorithm,array expertise: advanced author: chalarangelo cover: blog_images/antelope.jpg firstSeen: 2020-12-28T15:38:40+02:00 lastUpdated: 2020-12-29T16:32:46+02:00

npm

Lines of Code : 40

License : No License

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const kMeans = (data, k = 1) => {
  const centroids = data.slice(0, k);
  const distances = Array.from({ length: data.length }, () =>
    Array.from({ length: k }, () => 0)
  );
  const classes = Array.from({ length: data.length }, () =>

Plot k means cost matrix

python

Lines of Code : 60

License : No License

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def plot_k_means(X, K, index_word_map, max_iter=20, beta=1.0, show_plots=True):
    N, D = X.shape
    M = np.zeros((K, D))
    R = np.zeros((N, K))
    exponents = np.empty((N, K))

    # initialize M to random
    for k in range(K):
        M[k] =

plots k means for k means

python

Lines of Code : 56

License : No License

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def plot_k_means(X, K, max_iter=20, beta=3.0, show_plots=False):
    N, D = X.shape
    # R = np.zeros((N, K))
    exponents = np.empty((N, K))

    # initialize M to random
    initial_centers = np.random.choice(N, K, replace=False)
    M = X[initia

Generate a plot of k - means .

python

Lines of Code : 36

License : No License

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def plot_k_means(X, K, max_iter=20, beta=1.0):
    N, D = X.shape
    M = np.zeros((K, D))
    R = np.ones((N, K)) / K

    # initialize M to random
    for k in range(K):
        M[k] = X[np.random.choice(N)]

    grid_width = 5
    grid_height = ma

Community Discussions

Trending Discussions on K-Means

Eigenvectors and samples to calculate the points on PCA scale

How do I color clusters after k-means and TSNE in either seaborn or matplotlib?

Visualise in R with ggplot, a k-means clustered developmental gene expression dataset

Splitting image by whitespace

finding clusters in a picture opencv/python

K-Means centroids not visible in 3D clustering plot

How to plot the cluster centers?

Coordinates output to csv file python

How to plot scatter plot with original variables after scalling with K-means

PCA after k-means clustering of multidimensional data

QUESTION

Eigenvectors and samples to calculate the points on PCA scale

Asked 2022-Apr-10 at 19:04

I want to get the new points on the new scale for PC1 and PC2. I calculated the Eigenvalues, Eigenvectors and Contribution.

Now I want to calculate the points on the new scale (scores) to apply the K-Means cluster algorithm on them.

Whenever I try to calculate it by saying z_new = np.dot(v, vectors) (with v = np.cov(x)) I get a wrong score, which is [[14. -2. -2. -1. -0. 0. 0. -0. -0. 0. 0. -0. 0. 0.] for PC1 and [-3. -1. -2. -1. -0. -0. 0. 0. 0. -0. -0. 0. -0. -0.] for PC2. The right score scores (Calculated using SciKit's PCA() function) should be PC1: [ 4 4 -6 3 1 -5] and PC2: [ 0 -3 1 -1 5 -4]

Here is my code:

...

ANSWER

Answered 2022-Apr-10 at 19:04

The way I look at this, you have 6 samples with 14 dimensions. The PCA procedure is as follows:

1. Remove the mean

Starting with the following data:

Source https://stackoverflow.com/questions/71816829

QUESTION

How do I color clusters after k-means and TSNE in either seaborn or matplotlib?

Asked 2022-Mar-23 at 03:59

I have a dataframe that look something like this:

...

ANSWER

Answered 2022-Mar-23 at 03:59

With help from @tdy, I realized one of the solutions tried a little while ago was the solution I needed. My main problem was my edit 2, I wasn't graphing the right set of data. I changed the df to this:

Source https://stackoverflow.com/questions/71581368

QUESTION

Visualise in R with ggplot, a k-means clustered developmental gene expression dataset

Asked 2022-Feb-25 at 20:57

I can see many posts on this topic, but none addresses this question. Apologies if I missed a relevant answer. I have a large protein expression dataset, with samples like so as the columns: rep1_0hr, rep1_16hr, rep1_24hr, rep1_48hr, rep1_72hr .....

and 2000+ proteins in the rows. In other words each sample is a different developmental timepoint.

If it is of any interest, the original dataset is 'mulvey2015' from the pRolocdata package in R, which I converted to a SummarizedExperiment object in RStudio.

I first ran k-means clustering on the data (an assay() of a SummarizedExperiment dataset, to get 12 clusters:

...

ANSWER

Answered 2022-Feb-25 at 13:37

Here is my attempt at reverse engeneering the plot:

Source https://stackoverflow.com/questions/71265540

QUESTION

Splitting image by whitespace

Asked 2022-Jan-14 at 07:33

I have an image I am attempting to split into its separate components, I have successfully created a mask of the objects in the image using k-means clustering. (I have included the results and mask below)

I am then trying to crop each individual part of the original image and save it to a new image, is this possible?

...

ANSWER

Answered 2022-Jan-14 at 00:44

My solution involves creating a binary object mask where all the objects are colored in white and the background in black. I then extract each object based on area, from smallest to smallest. I use this "isolated object" mask to segment each object in the original image. I then write the result to disk. These are the steps:

Resize the image (your original input is gigantic)
Convert to grayscale
Extract each object based on area from largest to smallest
Create a binary mask of the isolated object
Apply a little bit of morphology to enhance the mask
Mask the original BGR image with the binary mask
Apply flood-fill to color the background with white
Save image to disk
Repeat the process for all the objects in the image

Let's see the code. Through the script I use two helper functions: writeImage and findBiggestBlob. The first function is pretty self-explanatory. The second function creates a binary mask of the biggest blob in a binary input image. Both functions are presented here:

Source https://stackoverflow.com/questions/70700974

QUESTION

finding clusters in a picture opencv/python

Asked 2022-Jan-03 at 12:15

looking at trying to find the center point of "clusters" / "circles" / "ellipses" ect in an image.

Example image:

It is clear by eye that there are 3ish clusters. I am looking to find the center point of the clusters, and the defining - rectangle, or circle (with radius) or ellipse, however to describe it.

Ideally, it is expanded to the following cases:

Unknown number of clusters (assume min 1, max 10) for simplicity if needed.

This picture is part of a larger picture that made use of canny-edge detection, thresholding and then contours to find me this area of interest. Unfortunately, I need it to be...more refined (or better parameters but I couldn't find anything that worked)

Things I have tried:

I have tried using kmeans segmentation, but they are mainly for color segments. And this image could be black/white for all the information the colors give us. HughCircles in opencv are not really the circles I want in that they give me way too many circles that matches "edges" Template matching via opencv also didnt work since it was too constrained and these cant be exact matches.

Any suggestions/avenues to examine would be welcome!
I have also attempted some basic scatterplot k-means clustering (as if this is data) but have not had good results so far.

Language of choice: python, but adaptable.

...

ANSWER

Answered 2022-Jan-03 at 12:15

I had an attempt at this and it may give you some ideas on how to proceed - even if only by seeing where it fails to work. I wanted to post it before the question gets a third close vote.

Source https://stackoverflow.com/questions/70540900

QUESTION

K-Means centroids not visible in 3D clustering plot

Asked 2021-Dec-21 at 17:00

When i plot clustering result in 2D using this code:

...

ANSWER

Answered 2021-Dec-21 at 17:00

The centroid stars are surrounded by the clouds of points, therefore are not easily visible. You can make them more visible by making the points smaller (with s=10 or smaller) and more transparent (with alpha=0.1 or smaller) as showed by the following code line:

Source https://stackoverflow.com/questions/70438687

QUESTION

How to plot the cluster centers?

Asked 2021-Dec-19 at 14:35

By using this k-means clustering from scratch, How can i plot the initial random cluster centers for k=3 on the scatter plot in the photo? Photo for Iris dataset

...

ANSWER

Answered 2021-Dec-19 at 14:35

You can draw the points and the centers via matplotlib's scatter function. Colors can be assigned depending on the group calculated via kmeans.

Here is an example (the kmeans function now also return the centroids).

Source https://stackoverflow.com/questions/70410267

QUESTION

Coordinates output to csv file python

Asked 2021-Dec-03 at 10:04

I am currently working on a project where i need to collect coordinates and transfer that to a csv file. I am using the k-means algorithm to find the coordinates (the centroids of a larger coordinate collection). The output is a list with coordinates. At first I wanted to simply copy it to an excel file, but that did not work as well as i wanted it to be.

This is my code:

...

ANSWER

Answered 2021-Dec-03 at 10:04

You could use the csv library as follows:

Source https://stackoverflow.com/questions/70211907

QUESTION

How to plot scatter plot with original variables after scalling with K-means

Asked 2021-Nov-08 at 18:27

I have scaled my original data X1:

...

ANSWER

Answered 2021-Nov-08 at 18:27

You can call scaler.inverse_transform() on the centroids. (Note that sns.scatterplot is an axes-level function and returns an ax, not a FacetGrid.)

Source https://stackoverflow.com/questions/69887575

QUESTION

PCA after k-means clustering of multidimensional data

Asked 2021-Oct-28 at 14:43

I have the following dataset with 10 variables:

I want to identify clusters with this multidimensional dataset, so I tried k-means clustering algorith with the following code:

...

ANSWER

Answered 2021-Oct-28 at 14:43

the problem is that you fit your PCA on your dataframe, but the dataframe contains the cluster. Column 'cluster' will probably contain most of the variation in your dataset an therefore the information in the first PC will just coincide with data['cluster'] column. Try to fit your PCA only on the distance columns:

Source https://stackoverflow.com/questions/69699120

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install K-Means

You can download it from GitHub.
You can use K-Means like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the K-Means component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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