k-means-clustering | data mining algorithm Constrained K | Topic Modeling library
kandi X-RAY | k-means-clustering Summary
kandi X-RAY | k-means-clustering Summary
Constrained K-means Clustering with Background Knowledge.
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Top functions reviewed by kandi - BETA
- Main method for testing
- Get the parameter as an Integer
- Parse CSV file
- Parse a constraint file
- Get param list
- Create a dimension from the given column
- Parse constraint files
- Checks if the clustering is acceptable
- Calculates the euclidean distance between this vector and another vector
- Returns a human - readable description of the given exception
- Get new clusters
- Gets the mean value of the clusterable items
- Returns the most common value in a list
- Cluster clusters
- Generate random clusters
- Checks if the clusterable items are equal
- Returns a String representation of the clusterable items
- Verify that a given clusterable is the same
- Returns a string representation of the dimensions
- Assigns the items to the closest clusterable
k-means-clustering Key Features
k-means-clustering Examples and Code Snippets
Lines of Code : 40
License : No License
Copyconst 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 }, () => def _kmeans_plus_plus(self):
# Points from only the first shard are used for initializing centers.
# TODO(ands): Use all points.
inp = self._inputs[0]
if self._distance_metric == COSINE_DISTANCE:
inp = nn_impl.l2_normalize(inp, Community Discussions
Trending Discussions on k-means-clustering
QUESTION
I've been trying to run RAPIDS on Google Colab pro, and have successfully installed the cuml and cudf packages, however I am unable to run even the example scripts.
TLDR;Anytime I try to run the fit function for cuml on Google Colab I get the following error. I get this when using the demo examples both for installation and then for cuml. This happens for a range of cuml examples (I first hit this trying to run UMAP).
...ANSWER
Answered 2021-May-06 at 17:13Colab retains cupy==7.4.0 despite conda installing cupy==8.6.0 during the RAPIDS install. It is a custom install. I just had success pip installing cupy-cuda110==8.6.0 BEFORE installing RAPIDS, with
!pip install cupy-cuda110==8.6.0:
I'll be updating the script soon so that you won't have to do it manually, but want to test a few more things out. Thanks again for letting us know!
EDIT: script updated.
QUESTION
I am performing a binary classification of a partially labeled dataset. I have a reliable estimate of its 1's, but not of its 0's.
From sklearn KMeans documentation:
...ANSWER
Answered 2020-Nov-20 at 20:14I'm reasonably confident this works as intended, but please correct me if you spot an error. (cobbled together from geeks for geeks):
QUESTION
I am using a k-modes model (mymodel) which is created by a data frame mydf1. I am looking to assign the nearest cluster of mymodel for each row of a new data frame mydf2.
Similar to this question - just with k-modes instead of k-means. The predict function of the flexclust package only works with numeric data, not categorial.
A short example:
...ANSWER
Answered 2020-Sep-29 at 09:08We can use the distance measure that is used in the kmodes algorithm to assign each new row to its nearest cluster.
QUESTION
I am new to machine learning and i am using
...ANSWER
Answered 2020-Aug-24 at 17:35Iris dataset contains 4 features describing the three different types of flowers (i.e. 3 classes). Therefore, each point in the dataset is located in a 4-dimensional space and the same applies to the centroids, so to describe their position you need the 4 coordinates.
In examples, it's easier to use 2-dimensional data (sometimes 3-dimensional) as it is easier to plot it out and display for teaching purposes, but the centroids will have as many coordinates as your data has dimensions (i.e. features), so with the Iris dataset, you would expect the 4 coordinates.
QUESTION
I'm trying to learn sklearn. As I understand from step 5 of the following example, the predicted clusters can be mislabelled and it would be up to me to relabel them properly. This is also done in an example on sci-kit. Labels must be re-assigned so that the results of the clustering and the ground truth match by color.
How would I know if the labels of the predicted clusters match the initial data labels and how to readjust the indices of the labels to properly match the two sets?
...ANSWER
Answered 2020-Mar-30 at 07:00With clustering, there's no meaningful order or comparison between clusters, we're just finding groups of observations that have something in common. There's no reason to refer to one cluster as 'the blue cluster' vs 'the red cluster' (unless you have some extra knowledge about the domain). For that reason, sklearn will arbitrarily assign numbers to each cluster.
QUESTION
I am testing this code.
...ANSWER
Answered 2020-Jan-03 at 01:33The problem may be with the format of your data. Most models will expect a data frame
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install k-means-clustering
You can use k-means-clustering 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-clustering 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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