Questions tagged [k-means]
k-means is a method to partition data into clusters by finding a specified number of means, k, s.t. when data are assigned to clusters w/ the nearest mean, the w/i cluster sum of squares is minimized
272 questions with no upvoted or accepted answers
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Where do initial document values come from in K-means document clustering?
So the K-means algorithm seems simple enough as I understand it: given some documents, turn those documents into points, initialize some number of k (centroids), assign document-points to nearest ...
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Using original centroid as cluster identifier after applying PCA
Take a look at my original data. (masked with purely random alphabetic here) :
a b c d e
f g h i j
A = k l m n o
p q r s t
u v w x y
I'm running ...
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Evaluating kmeans clustering with silhouette coefficient, weird results
I'm performing a kmeans clustering on a 22.000 documents datasets.
Not knowing how many clusters I should get, I ran different k values and try to assess the validity of the clusters by determining ...
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Estimating number of clusters using Gap Statistics
Since my application is for streaming data, I chose to use BIRCH to create clusters. BIRCH doesn't produce high quality results, therefore it requires "global clustering step" to improve output ...
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Calculating Error on a Hold-Out Set
I broke some data into a training and a hold out set. Then I clustered the training set with the k-means method. Now I want to calculate error using the holdout set. Do I just take the square the ...
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I want to classify data by distance from centroids in python
I'm making an image classifier that will tell if an image is a car or not, in Python.
here are my steps:
Get SIFT descriptors from about 200 images with cars on them.
On all those SIFT descriptors ...
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What could cause a K-means clustering algorithm to converge into a single cluster?
I am currently writing a K-means clustering algorithm in Python, and I seem to have coded myself into a corner... I begin my algorithm with data sets assigned randomly to the appropriate number of K ...
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Traffic Analytics using k-means
I'm going to provide some (near)real time analytics (classification) of the network traffic inside of my cluster. All traffic is aggregated into "session" and consists of some number of features. I've ...
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Streaming K-medoids
Mahout, Hadoop machine learning library, contains an implementation of Streaming K-means algorithm that is based on the following paperworks The Effectiveness of Lloyd-Type Methods for the k-Means ...
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Difference between criterion of k-means?
I am watching a video on k-means clustering here https://www.youtube.com/watch?v=sLf0Z9tCTjE&index=30&list=PL3DFCC23FCE3C7EFB, in which (12:14) the professor briefly mentioned some criterion ...
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How can I evaluate the accuracy of a clustering when I don't have information on the true class labels?
Already classified data set for the t-shirt factory problem
I want to calculate the accuracy of my algorithm. I have the training data without any size information and I couldn't find the classified ...
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Clustering groups that have replicated measures: hierarchical clustering on group-average VS regression tree
I measured 2 continous dependent variables (V1 and V2) on 10 occasions (10 replicates) for each of 4 groups.
I aim to cluster my groups. i.e. I dont want to cluster replicates, since this could mix ...
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How do I cluster documents using topic models?
Let us say I have a topic probability per document, for example:
...
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Unsupervised Clustering
My research is about comparing K-means and DBSCAN, and Im using unsupervised learning method in clustering.
Is it true that the number of cluster in K-means is also the same number as the unique ...
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how to classify input image using clustering algorithm such as k-mean?
I want to classify cifar10 images using a clustering algorithm (k-mean). Each image in the cifar10 dataset has a label, so, the results must be a set of labels which are corresponding to the test ...
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Device grouping using k-means to create clusters with overlapping neiborhoods
I want to use k-means to group (cluster) my devices into overlapping regions. For example I randomly generate the locations of my node devices on an $XY$ plane such as $n_1$ at location $(x_1,y_1)$, $...
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How are the data moving between clusters?
I am fairly new to cluster analysis and I have questions during the analysis.
I have used kmeans for my analysis.
I would like to explore how the data move through the clusters that I have ...
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Different types of variable scales in a cluster analysis ( nearest neighbours and k-means)
I've to conduct a cluster analysis (nearest neighbours and k-means) with different types of variables (metric, nominal and binary). Which transformation is appropriate for conducting a cluster ...
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I have this 3 clustering algorithms and I want to figure out which algorithm has the best algorithm for clustering
I'm new with clustering. I have this 3 algorithms and I want to figure out which algorithm has the best algorithm for clustering. I posted an image below, to show my clusters. I am confused on how to ...
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Kmeans for a data matrix containing both dense and sparse columns?
Assume the matrix contains one dense column, which consists of continuous values between 1-100. The other columns are binary values and are sparse. When applying Kmeans to such as matrix, does the ...
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k-means nstart equivalent for EM Clustering? Report only the best solution from a large number of initializations?
In K-means clustering, you can specify an nstart=i parameter, which performs the algorithm i times (i.e. selects the initial k random centroids i times) sand reports the best answer only. If I perform ...
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Which K-mean algorithm I have to use for this problem?
Perform a k-means Clustering (non-iterative algorithm) using k=2 randomly initialised centroids (cluster prototypes), and the Euclidean distance.
At the moment I manage to understand you can use ...
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