# Recent questions tagged k-means

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
How is it different from density-based clustering like DB Scan or hierarchical clustering such as K-Means?
How is it different from density-based clustering like DB Scan or hierarchical clustering such as K-Means?How is it different from density-based clustering like DB Scan or hierarchical clustering such as K-Means? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What do machine learning people mean by the term, "curse of dimensionality"?
What do machine learning people mean by the term, "curse of dimensionality"?What do machine learning people mean by the term, &quot;curse of dimensionality&quot;? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What is a key step in K-nearest neighbors?
What is a key step in K-nearest neighbors?What is a key step in K-nearest neighbors? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
In K-Means clustering, what does the acronym WCSS stand for?
In K-Means clustering, what does the acronym WCSS stand for?In K-Means clustering, what does the acronym WCSS stand for? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What are the new clusters and what are their centroids?
What are the new clusters and what are their centroids? Assume the following dataset is given: $(2,2), (4,4), (5,5), (6,6),(9,9) (0,4), (4,0)$. K-Means is run with $k=3$ to cluster the dataset. Moreover, ...
close
In general, K-means is limited to find clusters having complex shapes. What could be done to enable K-means to find clusters in arbitrary shapes (e.g. consider a post processing method)?In general, K-means is limited to find clusters having complex shapes. What could be done to enable K-means to find clusters in arbitrary shapes (e.g. ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
The complexity of K-Means is O(t*k*n*d). Explain!
The complexity of K-Means is O(t*k*n*d). Explain!The complexity of K-Means is O(tknd). Explain! ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
K-Means does not explicitly use a fitness function. What are the characteristics of the solutions that K-Means finds --- which fitness function does it implicitly minimize?
K-Means does not explicitly use a fitness function. What are the characteristics of the solutions that K-Means finds --- which fitness function does it implicitly minimize?K-Means does not explicitly use a fitness function. What are the characteristics of the solutions that K-Means finds --- which fitness function does i ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
Is it possible for the k-means algorithm to revisit a configuration?
Is it possible for the k-means algorithm to revisit a configuration?Let a configuration of the k means algorithm correspond to the k way partition (on the set of instances to be clustered) generated by the clustering a ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What is parameter tuning when using k-nearest-neighbour algorithm?
What is parameter tuning when using k-nearest-neighbour algorithm?What is parameter tuning when using k-nearest-neighbour algorithm? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
How do I choose k when using k-nearest neighbor algorithm?
How do I choose k when using k-nearest neighbor algorithm?How do I choose k when using k-nearest neighbor algorithm? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What is K-means clustering?
What is K-means clustering?What is K-means clustering? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What does the algorithm mind map say?
What does the algorithm mind map say?What does the algorithm mind map say? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What are 3 ways of reducing dimensionality?
What are 3 ways of reducing dimensionality?What are 3 ways of reducing dimensionality? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What is the "Curse of Dimensionality?"
What is the "Curse of Dimensionality?"What is the &quot;Curse of Dimensionality?&quot; ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
How is KNN different from k-means clustering?
How is KNN different from k-means clustering?How is KNN different from k-means clustering? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
Why is a Silhouette plot good for visualizing clusters from k-means algorithm?
Why is a Silhouette plot good for visualizing clusters from k-means algorithm?Why is a Silhouette plot good for visualizing clusters from k-means algorithm? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
How does one choose the $k$ when using the k-means algorithm?
How does one choose the $k$ when using the k-means algorithm?How does one choose the $k$ when using the k-means algorithm? ...
close

Notice: Undefined index: avatar in /home/customer/www/mathsgee.com/public_html/qa-theme/AVEN/qa-theme.php on line 993
What is the advantage of performing dimensionality reduction before fitting an SVM?